Patent Grant
8229933
The present invention is directed towards management of on-line advertising contracts based on targeting.
The marketing of products and services online over the Internet through advertisements is big business. Advertising over the Internet seeks to reach individuals within a target set having very specific demographics (e.g. male, age 40-48, graduate of Stanford, living in California or New York, etc). This targeting of very specific demographics is in significant contrast to print and television advertisement that is generally capable only to reach an audience within some broad, general demographics (e.g. living in the vicinity of Los Angeles, or living in the vicinity of New York City, etc). The single appearance of an advertisement on a webpage is known as an online advertisement impression. Each time a webpage is requested by a user via the Internet represents an impression opportunity to display an advertisement in some portion of the webpage to the individual Internet user.
Some advertisers enter into contracts with an ad serving company (or publisher) to receive impressions. An advertiser may specify desired targeting criteria. For example, an advertiser may enter into a guaranteed delivery contract with the ad serving company, and the ad serving company may agree to post 2,000,000 impressions over thirty days for US$15,000. In some cases, an advertiser will choose to enter into a non-guaranteed contract with the ad server company and only pay for those impressions actually made by the ad serving company on their behalf. Of course, in modern Internet advertising systems, the competition among advertisers for placement of impressions under non-guaranteed contracts is often resolved by an auction, and the winning bidder's advertisements are shown in the available spaces of the impression.
Online advertising and marketing campaigns often rely, at least partially, on a process where any number of advertisers book contracts with the intention to reach users who satisfy some particular targeting criteria (e.g. male, age 40-48, graduate of Stanford, living in California or New York, etc). Matching a contract to a user can be thought of as a market function, where a user visit is a unit of supply, and a contract is a unit of demand. The market is served by matching supply to demand (or demand to supply). The matching of supply to demand applies to contextual advertising (e.g. text and graphical ads that match a page context and user impression) as well as to sponsored search advertising (e.g. ads that match with search engine queries and results). Various degrees of matching may occur when a user's attribute is matched against an advertiser's targeting criteria.
Considering that (1) the actual existence of a webpage impression opportunity suited for displaying an advertisement is not known until the user clicks on a link pointing to the subject webpage, and (2) that the matching process for selecting advertisements must complete before the webpage is actually displayed, it then becomes clear that the process of assembling competing contracts, completing the matching, and compositing the webpage with the advertiser's ads must start and complete within a matter of fractions of a second. Thus, a system that rapidly matches contracts to opportunities is needed.
Other automated features and advantages of the present invention will be apparent from the accompanying drawings, and from the detailed description that follows below.
A method for matching of contracts using a fixed-length complex predicate representation for evaluation by projecting TRUE nodes onto a discrete set of symbols. A computer-implemented method comprises storing (in a computer memory), an impression opportunity profile in the form of a Boolean expression and converting such an impression opportunity profile into a conjunct-level representation of impression conjuncts (e.g. a list). The method includes steps for retrieving a set of candidate contracts that match impression conjuncts, and constructing an AND/OR contract tree representation of contracts from among the set of candidate contracts, the AND/OR contract tree comprising a plurality of nodes representing contract tree leaf node predicates, with each contract tree leaf node predicate having a fixed-length label representing a projection onto a discrete set of ordered symbols. Contract tree leaf node predicates are evaluated against the list of impression conjuncts and, based on a comparison (e.g. a threshold comparison, a multi-value comparison, etc.), matching contract tree leaf node predicates are marked as TRUE. The desired results (i.e. identifying satisfying contracts that match the impression) are obtained by projecting the label assigned to the TRUE/marked contract tree leaf node predicates over the discrete set of ordered symbols. The satisfying contracts are those where the projecting operation results in a contiguous projection of the fixed-length label over the discrete set of ordered symbols.
The novel features of the invention are set forth in the appended claims. However, for purpose of explanation, several embodiments of the invention are set forth in the following figures.
In the following description, numerous details are set forth for purpose of explanation. However, one of ordinary skill in the art will realize that the invention may be practiced without the use of these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to not obscure the description of the invention with unnecessary detail.
Section I: General Terms and Network Environment
In the context of Internet advertising, bidding for placement of advertisements within an Internet environment (e.g. system 100 of
In the slightly more sophisticated model of
Therefore, multiple competing advertisers might elect to hid in a market (e.g. an exchange) via an exchange server or auction engine 107 in order to win the most prominent spot, or an advertiser might enter into a contract (e.g. with the Internet property, or with an advertising agency, or with an advertising network, etc) to purchase the desired spots for some time duration (e.g. all top spots in all impressions of the webpage empirestate.com/hotels for all of 2010). Such an arrangement and variants as used here is termed a contract.
In embodiments of the system 150, components of the additional content server, perform processing such that, given an ad opportunity (e.g. an impression opportunity profile predicate), processing determines which (if any) contracts match the ad opportunity.
In some embodiments, the system 150 might host a variety of modules to serve management and control operations (e.g. objective optimization module 110, forecasting module 111, data gathering and statistics module 112, storage of advertisements module 113, automated bidding management module 114, admission control and pricing module 115, impression and contract tree construction module 116, and matching and projection module 117, etc) pertinent to contract matching and delivery methods. In particular, the modules, network links, algorithms and data structures embodied within the system 150 might be specialized on as to perform a particular function or group of functions reliably while observing capacity and performance requirements. For example, an additional content server 108 in conjunction with an auction engine 107 might be required to select a set of top N contracts that satisfy a complex target description and complete an auction to identify a winner. The top N contracts might be selected from a database (e.g. index) of many thousands or millions of contracts, and the complex target description might involve dozens, or hundreds, or even more attributes and values; further, the selection of contracts and completion of the auction might have to begin and end within a period of a fraction of a second.
In order for a contract for delivery of one or more impressions to be satisfied, there should exist specific inventory to be delivered under the terms of the contract. In the case of online Internet advertising, an item of inventory (e.g. an impression) might be specified in an arbitrarily complex description that might involve dozens, or hundreds, or even more attributes and values, which attributes and values are to be matched to one or more matching contracts.
As shown in
In some cases, a fine degree of specificity is useful in targeted advertising. For example, an advertiser for a hotel in mid-town New York City might want to place advertisements only on the empirestate.com/hotels webpage as shown to an Internet user, and then only if the Internet user is from California, and then only if the Internet user is male, and so on. Such an advertiser might be willing to pay a premium for a spot that is most prominently located on the webpage. In fact, such an advertiser might be joined by other hoteliers who also want their advertisements to be displayed in the most prominently located spot on the webpage.
A contract might be as simple as the contracts described in the previous example, or a contract might be more complex, possibly describing a target (at least n part) using an arbitrarily complex expression involving many terms (e.g. attributes, possibly many attribute values, and possibly any number of multi-valued attributes). A contract might also specify or require varying degrees of confidence that a particular contract term is satisfied (e.g. confidence that a target is male is 90%, confidence that a target is domiciled in California is 80%).
Of course, a contract might be represented in a significantly more complex Boolean expression, possibly using arbitrarily complex operators involving multi-value operators and confidence operators. For example, the contract {gender IN {Male} AND topic IN {Life, News} AND income IN {50 k-100 k} AND clickHistory {Active} AND gee IN {Santa Clara {60%}}AND New York {99%}} includes both a multi-value operator (e.g. topic IN {Life, News}) as well as a multi-value operator that also includes confidence metrics (e.g. geo IN {Santa Clara {60%}, New York {99%}}).
What is needed are techniques that enable contracts expressed as complex predicates to be matched to impression opportunities expressed as complex predicates. Indeed increased targeting using complex predicates allows advertisers to reach more relevant customers. For example, an advertiser selling family fitness aids might specify a target using broad targeting constraints such as “1 million Yahoo! users from 1 Aug. 2008-31 Aug. 2008”. In contrast, an advertiser selling fitness aids for surfers might specify a much more fine-grained constraint such as “10,000 Yahoo! users from 1 Aug. 2008-8 Aug. 2008 who are California males between the ages of 20-35 who are working in the healthcare industry and like surfing and autos”.
Given such an environment, the admission control portion of admission control module 310 serves to generate quotes for guaranteed contracts and accept bookings of guaranteed contracts, the pricing portion of admission control module 310 serves to price guaranteed contracts, the ad serving portion of ad server and bid generation module 320 selects guaranteed ads for an incoming opportunity, and the bidding portion of ad server and bid generation module 320 submits bids for he selected guaranteed ads on an exchange 340. Additionally, an optimizer 390 might communicate with a plan distribution and statistics gathering module 350, and one or more forecasting modules 360, 370, 380 and return results that optimize for an overall objective.
Given the system 300 of
The admission control module supports queries and other interactions with sales personnel who quote guaranteed contracts to advertisers and book the resulting contracts. A sales person issues a query with a specified target (e.g. “100,000 Yahoo! users from 1 Aug. 2008-8 Aug. 2008 who are California males between the ages of 20-35 who are working in the healthcare industry and like surfing and autos”). The admission control module 310 returns the available inventory for the target and returns the associated price for the available inventory. The sales person can then book corresponding contracts accordingly. The ad server and bid generation module 320 takes in an opportunity (e.g. an impression opportunity), and returns an ad corresponding to the opportunity along with the amount that the system is willing to bid for that opportunity in the spot market (the Exchange).
In one embodiment, the operation of the entire system 300 is orchestrated by an optimization module 390. This optimization module 390 periodically takes in a forecast of supply (future impression opportunities), guaranteed demand (expected guaranteed contracts), and non-guaranteed demand (expected bids in the spot market) and matches supply to demand using an overall objective function. The optimization module then sends a plan of the optimization result to the admission control module 310. Of course, inasmuch as the plan is based on statistics relating to data gathered over time, the plan is updated every few hours based on new estimates for supply, new estimates of demand, and new estimates for deliverable impressions.
In another scenario, and one that relates to techniques for finding all applicable contracts (i.e. guaranteed as well as non-guaranteed contracts), bringing their respective bids to the unified marketplace might operate in a scenario described as follows:
When a sales person issues a query (e.g. to the admission control module 310) for some contract (e.g. including a target specification and duration) for future delivery (i.e. guaranteed or non-guaranteed), the system 300 invokes the supply and forecasting module 360 to identify how much inventory is available for that contract. Since targeting queries can be very fine-grained in a high-dimensional space, the supply forecasting module might employ a scalable multi-dimensional database indexing technique to capture and store the correlations between different targeting attributes. The scalable multi-dimensional database indexing technique might also serve to capture and retrieve correlations found among multiple contracts. For example, if there are two sales persons submitting contracts in contention (e.g. “Yahoo! finance users who are California males” and “Yahoo! users who are aged 20-35 and interested in sports”), some number of forecasted impression opportunities might match both contracts, but of course the inventory of matching impression opportunities should not be double-counted. In order to deal with contract contention for supply in a high-dimensional space, the supply forecasting system might produce impression samples (i.e. a selected subset of the total available inventory) as opposed to just available inventory counts. Thus, impression opportunity samples from available inventory might be used to determine how many contracts can be satisfied by each impression opportunity. Given the impression samples, the admission control module uses the plan to calculate the extent of contention between contracts in the high-dimensional space. Finally, the admission control module 310 might return allocated available inventory to each of the sales persons without any double-counting. In addition, the admission control module might calculate the price for each contract and return pricing along with the quantity of allocated impression opportunities.
Now, stating the problem to be solved more formally, given an advertising opportunity (e.g. an impression opportunity), specified as a predicate or Boolean expression (e.g. a vector, a list, a set of attributes each of which may have one or more associated values, etc) including assignments of one or more attributes to one or more values, find all of the contracts that could bid on this opportunity. For example, given the impression opportunity profile predicate {(state=CA) AND (gender=male) AND (age=50)}, some possibly matching contracts would include those asking for {(gender=male) AND (state=CA)}, and would include those asking for {(gender=male) AND {(age=50)} because each clause of each of those contracts are satisfied against the example impression opportunity profile predicate. The embodiments of the invention herein permits both disjunctive as well as conjunctive types of contracts, and even contracts including more complex predicates, to be handled efficiently. As regards contracts including complex predicates, embodiments of the invention disclosed herein support “IN” operators (e.g. state IN (NY, CA, MA)) and “NOT-IN” operators (e.g. state NOT-IN (NY, CA, MA)), as well as confidence operators (e.g. driverimported=“male” {confidence 30%}).
In various embodiments, a contract might be specified in some arbitrarily complex logic expression (e.g. involving any number of multiple-predicate expressions) which expression can be mathematically transformed (e.g. decomposed, normalized) into a disjunctive normal form (DNF) or into a conjunctive normal form (CNF). A contract specified as a DNF expression contains any number of “OR” terms, any one of which, if satisfied, satisfies the specification of the contract. A contract specified as a CNF expression contains any number of “AND” conjunctions, such that all conjunctions must be satisfied in order to satisfy the specification of the contract. Once a contract has been normalized (i.e. into DNF or into CNF), each term can be considered a subcontract. To handle contracts in DNF (OR-ing), the techniques disclosed herein might split a contract into subcontracts (one for each term), and produce an index entry for each of the subcontracts. To support contracts in CNF (AND-ing), the techniques disclosed herein might check to confirm that each of the subcontracts corresponding to its contract is found in the index.
Section II: Detailed Description of the Problem Solved by an Efficient Inverted Index System
As indicated in the foregoing, one application served by the construction of an efficient inverted index system is related to booking and satisfying online advertisement contracts. It should be emphasized that the time period between an Internet user's click on a link and the display of the corresponding page—including any advertisements is a short period—desirably a fraction of a second. It is within this short time period that applicable contracts must be identified, some or all of those contracts compete for spots on the soon-to-be-displayed webpage, the winner's or winners' advertisements are selected and placed in the webpage, and finally the webpage is rendered at the user's terminal. Thus, an efficient inverted index might be efficient as measured by latency, as well as efficient with respect to computing cycles, especially when many contracts may be booked at any given moment in time.
Further, the inverted index system may receive any arbitrarily complex expressions that describe a contract. The indexing and matching techniques disclosed herein address at least solving the lookup and contract-matching problem efficiently and even under conditions where the input data (e.g. a contract predicate, an impression predicate) is complex.
Syntax and Construction of Contracts and Impression Opportunities
Following the foregoing discussion, a contract can be described in a Boolean expression using IN, NOT-IN, and {confidence} operators as basic operators. An impression opportunity is a set of one or more points within a multi-dimensional space where any single point can be described using finite domains for each attribute along a dimension.
Section III: Syntax Used in Construction of an Inverted Index
Contract Syntax Using Basic Predicates
As described herein, there are several types of basic predicate operators: Equality predicates, IN predicates, and NOT-IN predicates. For example, state=CA says that the state is CA, the predicate state IN {CA, NY} says that the state could either be CA or NY, and the predicate state NOT-IN {CA, NY} indicates the state could be anything other than CA or NY. It is important to observe that state IN {CA, NY} is equivalent to state IN {CA} state IN {NY} (making it a disjunction of length 2) while state NOT-IN {CA, NY} is equivalent to state NOT-IN {CA}
state NOT-IN {NY} (making it a conjunction of length 2). Notice that IN and NOT-IN predicates also cover equality and non-equality predicates. Other basic predicate operators might also be supported. Ranges of integers can be supported by mapping them into equality. For example, using the demographic for a person in the age range 18-24, that age range might be mapped to a single value. Thus an age range can be described in an IN or NOT-IN predicate. For example, the age range 18-24 might be mapped to value r3, the age range 25-32 might be mapped to value r4, etc. Other demographics that are expressed as ranges might also be mapped into symbolic, string, or integer values, etc. Thus the characteristic of annual income in the range $22 k to $56 k per year might be mapped to income=3.
In some basic forms, a contract is a DNF or CNF expression on the two basic expressions IN and NOT-IN. For example, (state IN {CA, NY} age IN {20})
(state NOT-IN {CA, NY}
interest IN {sports}) is a DNF expression using the two types of atomic expressions while (state IN {CA, NY}
age IN {20})
(interest IN {sports}) is a CNF expression. Notice that a conjunction can either be a DNF expression with one disjunct or a CNF expression with conjuncts of size 1.
Impression Opportunity Profile Predicate Types
A simple impression opportunity profile of an impression opportunity includes a set of attributes and corresponding single value pairs. For example, {state=CA age=20
interest=sports} is a simple impression opportunity profile. A simple impression opportunity profile describes only a single point in a multi-dimensional space. That is, within a predicate describing a simple impression opportunity profile, each attribute used to describe an impression opportunity profile is expressed with a corresponding single value.
A multi-valued impression opportunity profile predicate of an impression opportunity includes at least one expression of an attribute with a corresponding multi-value set. For example, {state=IN{CA, AZ} age=20
interest=sports} is a multi-valued impression opportunity profile since the conjunction state=IN{CA, AZ} expresses the attribute state with its corresponding multi-value set IN {CA, AZ}. A multi-valued profile of an impression opportunity describes multiple points in a multi-dimensional space. Any number of expressions of an attribute with a corresponding multi-value set and/or any number of expressions with a corresponding single value may be combined to form a multi-valued impression opportunity profile predicate.
Section IV: Index Construction for Matching Satisfying Contracts to Impression Opportunities Using Complex Predicates
In one embodiment, construction of an inverted index may commence by making posting lists of contracts for each IN predicate. For each attribute name and single value pair of an IN predicate, we make one posting list. Hence, the index structure “flattens” the IN predicates when constructing the posting lists. In many of the embodiments described herein, the inverted index is sorted. Furthermore, each posting list might sort its contracts by contract id, and the posting lists themselves might be sorted by the ids of their current contracts. Of course other ids or keys might be used for sorting the posting lists and/or for sorting contracts within a posting list, and such alternative ids and keys are possible and envisioned. For example, contracts might be sorted by any arbitrary key, such as customer type.
ε p.list do
Consider the two contracts in Table 1. For each attribute name and possible value, Algorithm 1 constructs a posting list of contracts with flags. The final inverted index is shown in Table 2. Notice how all the IN predicates are flattened out into single values. Each posting list has its contracts sorted, and the posting lists themselves are also sorted according to the contracts they have.
state IN {CA}
state IN {NY}
The Counting Algorithm
In an embodiment known as The Counting Algorithm, the algorithm is applied on contract expressions in the form of conjunctions. The idea is to maintain a counter for each contract on how many predicates of the contract are satisfied. The inverted index for the conditions of the impression opportunity is scanned once. This algorithm can be considered as a baseline algorithm for performance comparison. Notice that the Counting Algorithm can support NOT-IN predicates by modifying Step 8 of Algorithm 2, namely by setting the Count value to minus infinity if the contract is tagged NOT-IN.
Consider the impression opportunity I={age=1state=CA}. Given the inverted index in Table 2, the posting lists for I are shown in Table 3. Scanning through the posting lists and incrementing the counters for each contract results in the final counts as shown in Table 4.
For each contract in Table 4, compare the count value with the number of predicates in the contract (i.e. the size of the contract). As a result, contracts c1, c3, and c4 are satisfied by I because their counts are equal to their sizes.
Complexity:
The complexity of the Counting algorithm is linear to the sum of the posting list sizes of P:
O(Σk=0 . . . |P|−1|P[k]|)
The WAND Algorithm
Another embodiment uses a variant of the WAND algorithm [Broder et al.] The WAND algorithm assumes a conjunction of IN predicates for contracts. Compared to the Counting algorithm, WAND makes the following improvements.
In this algorithm, contracts of size K=0 (i.e. there are no predicates), are deemed to always match. Since contracts of size K=0 do not appear in the posting lists, a separate posting list (called Z) that contains all contracts of size 0 is maintained. When K=0, Z is always returned by the idx.GetPostingLists method.
In the examples following, the posting lists are denoted for contracts of size K as PK. For example, the posting lists for contracts of size 2 is denoted as P2.
Algorithm 3 extracts the posting lists of I from idx. This time, however, the algorithm extracts posting lists for each possible size of contract. In Table 1, there are shown two sizes of contracts: size K=1 contains the set of contracts (c3, c4) and size K=2 contains the set of contracts (c1, c2). Hence, Table 5 shows two sets of posting lists for each size. The current contract of each posting list is underlined. Notice that in this example, the posting lists are in sorted order according to their contract IDs.
c1 → c2
Processing continues by processing P1, that is, the posting lists of contracts with size 1. Since P1[0].Current.ID=P1[0].Current.ID=3 at Step 15, this example adds c3 to O in Step 16. The algorithm then skips all the posting lists to c4 because P[0].Current.ID+1=3+1=4. Hence, P1[0] reaches the end of the list while P1[1] still has c4 as its current contract. The posting lists after sorting P1 are shown in Table 6. Notice that the posting list of (age, 1) is placed at the end because it is done with processing. Since P1[0].Current.ID=P1[0].Current.ID=4 at Step 15, c4 is also accepted and included in O. After advancing the posting list P1[0], the algorithm exits the while loop in Step 13.
Next, process P2 in the second for loop. Since K is 2 and P2[0].Current.ID=P2[1].Current.ID=1, Step 16 adds c1 to O. Since NextID is 2, we advance both posting lists in P2 to C2. Notice that the posting list with key (state, CA) does not contain c2 and thus points to null, i.e. the end of the list. The posting lists after sorting P2 in Step 14 are shown in Table 7. This time, P2[0].Current=c2 while P2[1].Current=null, so go back to Step 13. Since P2[1].Current=null, terminate the while loop and return O={c1, c3, c4} as the result.
Complexity:
Although WAND improves the Counting algorithm by using skipping and partitioning techniques, its complexity is actually greater than that of the Counting Algorithm. In the worst case, the WAND Algorithm needs to sort the posting list P while advancing one posting list in Step 22. Sorting in Step 14 actually takes logarithmic time to |P| because the inverted index is initially sorted, and it is only needed to bubble down one posting list in P using a heap to maintain a sorted order for each posting list advanced. Hence, the complexity becomes
O(log(|P|)×Σk= . . . |P|−1|P[k]|)
The WAND Algorithm and variants are disclosed in commonly-owned US patent application entitled “System and Method for Automatic Matching of Highest Scoring Contracts to Impression Opportunities Using Complex Predicates and an Inverted Index” filed Jul. 14, 2009 under Ser. No. 12/502,742, which application is hereby incorporated by reference for all purposes. In particular, variants of the WAND Algorithm provide efficient support for indexing including NOT-IN predicates in arbitrarily complex DNF or CNF expressions.
Section V: Index Construction for Matching Highest Scoring Contracts to Impression Opportunities Using Complex Predicates
As indicated above, the WAND Algorithm has been extended to include building an inverted index of contracts when the set of contracts contains targets reduced to CNF expressions, even when containing NOT-IN predicates. Still further improvements are possible and envisioned. In particular, the disclosure of this section provides several approaches to handling an inverted index that includes weighting. Suppose each contract, in addition to being specified with any arbitrarily complex Boolean expression (BE) also has an association with one or more weighting coefficients, which coefficients can be used in a quantitative calculation of a goodness score. The ability to calculate a goodness score implies that not all contracts that satisfy some particular Boolean expression need be regarded as equal. The inverted index embodiments of Section IV serve for efficiently retrieving all matching contracts. The algorithms and data structures are applied and extended for efficiently retrieving the top N contracts.
One approach for retrieving the top N contracts would be to first find all of the matching contracts, calculate the goodness score for each, then sort by the goodness score and return only the top N. As aforementioned, the total number of matching contracts may be a large number (e.g. in the hundreds or thousands or more), thus, the application of such an approach involves significant computational power for scoring the total number of matching contracts, even though the number of top N contracts might be a quite small number (e.g. 5, 10, 20, etc). Techniques for matching highest scoring contracts to impression opportunities are disclosed in commonly-owned US patent application entitled “System and Method for Automatic Matching of Highest Scoring Contracts to Impression Opportunities Using Complex Predicates and an Inverted Index” filed Jul. 14, 2009 under Ser. No. 12/502,742, which application is hereby incorporated by reference for all purposes.
Scoring
The weighted score of a BE E reflects the “relevance” or goodness of E to an assignment (i.e. an assignment being an impression opportunity) S. For example, a user interested in sports might be more interested in an advertisement for sport shoes than an advertisement for flowers. If E is a conjunction of ε and ∉ predicates, the score of E is defined as
Scoreconj(E,S)=Σ(A,v)εIN(E)∩SwE(A,v)×wS(A,v)
where IN(E) is the set of all attribute name and value pairs in the ε predicates of E (scoring ∉ predicates is ignored and wE (A,v) is the weight of the pair (A,v) in E). Similarly, wS(A,v) is the weight for (A,v) in S. For example, a BE ageε{1,2}stateε{CA} could be targeting young people in California, giving the pair (age,1) a high weight of 10 while giving (age,2) a lower weight of 5 and (state, CA) a weight of 3. If there is an assignment {age=1,state=CA}, where the first pair has a weight of 1 while the second pair has a weight of 2, the score of the BE to the assignment is 10×1+3×2=16.
In order to do top-N pruning, an upper bound UB(A,v) is generated for each attribute name and value pair (A,v) such that
UB(A,v)≧max(wE
For instance, if UB(age,1)=10, then (age,1) may not contribute more than a weight of 10 regardless of the BE.
DNF Scoring
The score of a DNF BE E is defined as the maximum of the scores of the conjunctions within E where E.i denotes the ith conjunction of E and |E| the number of conjunctions in E
ScoreDNF(E,S)=maxi=1 . . |E|Scoreconj(E.i,S)
Intuitively, the DNF score is equal to the contribution of just one conjunction, that being the conjunction scoring the highest from among the group of conjunctions comprising the DNF expression.
CNF Scoring
The score of a CNF BE E is similar to Scoreconj and is defined as the sum of the disjunction scores (using ScoreDNF) within E where E.i denotes the ith disjunction of E and |E| the number of disjunctions in E.
ScoreCNF(E,S)=Σi=1 . . . |E|ScoreDNF(E.i,S)
Intuitively, the CNF score combines all the contributions of each disjunction.
Inverted List Construction for DNF Representations
The discussion below describes how to build an inverted index data structure on the conjunctions of the BEs. First, create predicate size partitions by partitioning all the conjunctions by their sizes (i.e. number of predicates). The partition with conjunctions of size K are referred to as the K-index. Then, for each K-index, create posting lists for all possible attribute name and value pairs (also called keys) among the conjunctions. A posting list head contains the key (A,v). In an exemplary embodiment, each entry of a posting list represents a conjunction c and contains the ID of c as well as a bit indicating whether the key (A,v) is involved in an ε or ∉ predicate in c A posting list entry e1 is “smaller” than another entry e2 if the conjunction ID of e1 is smaller than that of e2. In the case where both conjunction IDs are the same (in which case e1 and e2 appear in different lists), e1 is smaller than e2 only if e1 contains a ∉ while e2 contains an ε. Otherwise, the two entries are considered the same. Using this ordering, the entries in a posting list are sorted in increasing entry order, while in each K-index, the posting lists themselves are sorted in increasing entry order of their first entry. Notice there are no two entries with the same conjunction ID within the same posting list because an attribute is only allowed to occur once in each conjunction. Keeping the posting lists sorted in each K-index reduces the sorting time of posting lists as is performed in some of the algorithms presented herein (e.g. as in the Conjunction Algorithm, shown below).
As a special case, conjunctions of size 0 (e.g. age ∉{3} is a conjunction of size 0 because it has no ε predicates) are all included in a single posting list called Z. This special posting list is needed to ensure that zero-sized conjunctions appear in at least one posting list given an assignment. In addition, each entry in Z contains an ε predicate. This modification ensures that Algorithm 11 also works for zero-sized conjunctions.
Consider the conjunctions in Table 8. The conjunctions are first partitioned according to their sizes (c1,c2,c3,c4 each have a size of 2, c5 has a size of 1, and c6 has a size of 0). For each size partition K=0, 1, 2 . . . , Table 9 shows the construction of the K-indexes. For instance, the key (age,4) has a posting list inside the partition K=1 and contains an entry representing c5. Notice that the weight for any entry that has a NOT-IN indication (i.e. ∉) is partitioned into the K=0 partition because NOT-IN predicates are not considered for scoring.
state ε {NY}
gender ε {F}
gender ε {M}
state ∉ {CA}
gender ε {M}
Section VI: Storing the Ranking of Boolean Expressions within an Inverted Index
DNF Ranking Algorithm
Ranking DNF BEs can be performed by maintaining a top-N queue of conjunctions and restricting them to have unique DNF IDs within the queue. Since the score of a DNF BE is the maximum score of its conjunction scores, the inverted index needs only to keep the single highest conjunction score for each DNF ID.
Referring to the weights in the inverted list representation of Table 9 to rank BEs, the number next to each posting list key (A,v) denotes the upper bound weight UB(A,v). In each posting list entry, the third value denotes the weight wc (A,v) for conjunction c. For example, the key (age,4) in Table 9 has a posting list inside the partition K=1 and contains an entry representing c5 where wc
Algorithms can be extended to efficiently deal with weights by adding pruning techniques.
Given the assignment S:{age 3, state=NY, gender=F}, the matching posting lists for K=2 from the inverted lists of Table 9 are shown in Table 10. Notice the assignment weight coefficients in the first column. As shown, the weights are wS(state, NY)=1.0, wS(age,3)=0.8, and wS(gender, F)=0.9. Consider the example of N=1 (i.e. only the conjunction with the single highest score is maintained). The score of c1 is w1(state, NY)×wS(state, NY)+w1(age,3)×wS(age,3)=4.0×1.0+0.1×0.8=4.08. The Nth highest score is thus set to 4.08.
(1, ε, 4.0)
(1, ε, 0.1) (2, ε, 0.1) (3, ε, 0.2)
(2, ε, 0.3)
A first pruning technique is illustrated in Table 11 where the posting lists are sorted after accepting c1. Before checking whether the first and second posting lists have the same conjunction in their current entries, the algorithm computes the upper bound score of c2 by computing UB(age,3)×wS(age,3)+UB(gender,F)×wS(gender,F)=1.0×0.8+2.0×0.9=2.6. Since 2.6 is smaller than the Nth score 4.08, the algorithm skips (i.e. prunes) the first two posting lists. In this way, pruning is accomplished by comparing a first upper bound score (e.g. the upper bound score of contract c2) to a second upper bound score (e.g. the upper bound score of the Nth of top N contracts).
(2, ε, 0.3)
A second pruning technique is illustrated in Table 12, which shows the posting lists for K=1. Before processing the posting lists, first derive the upper bound score for all the conjunctions in the K-index by computing UB(age,3)×wS(age,3)=1.0×0.7=0.7. Since an upper bound score of 0.7 is less than the current Nth score 4.08, skip processing (i.e. prune) the posting lists for K=1. Similarly, K=0 (not shown) can also be skipped to return the final solution which has the highest score 4.08.
(5, ε, 0.1)
CNF Ranking Algorithm
Ranking CNF BEs can be performed by maintaining a top-N queue of CNF BEs. In fact, the first pruning technique of the DNF ranking algorithm can be applied. Since the score of a CNF BE is the sum of the disjunction scores while the score of a disjunction is the maximum score of its predicates, the sum UB(A,v)×wS(A,v) for the corresponding posting lists is still an upper bound for the.
However, the technique of computing the upper bound score as discussed in the DNF ranking algorithm does not apply directly to the CNF ranking algorithm because more than K disjunctions may contribute to the score of a CNF with size K (i.e. disjunctions that contain both ε and ∉ predicates do not count in the size of the CNF, but such predicates may have scores that add to the CNF score). Hence, the sum of the top-K UB(A,v)×wS(A,v) values is not an upper bound score of a CNF BE. Rather, he upper bound score of a CNF BE is calculated as the sum of the disjunction scores.
Given the assignment S:{A=1,C=2}, the matching posting lists for K=2 from the inverted list of Table 34 are shown in Table 38 along with the given assignment weight coefficients wS(A,1)=0.1 and wS (C,2)=0.9. As earlier discussed, the only matching CNFs in Table 38 are c3 and c4. In this example, after accepting c3 and deriving the score w3(A,1)×wS(A,1)+w3(C,2)×wS(C,2)=0.3×0.1+2.7×0.9=2.46, this pruning technique skips processing CNF ID 4 from Step 16 because the upper bound of c4 is UB(A,1)×wS(A,1)+UB(A,1)×wS(A,1)=0.5×0.1+0.5×0.1=0.1, which is smaller than 2.46.
(1, ε, 0, 0.1) (2, ε, 0, 0.3) (3, ε, 0, 0.3) (4, ε, 0, 0.1)
(2, ε, 0, 2.5) (3, ε, 1, 2.7)
(4, ε, 1, 0.1)
Section VII: Automatic Matching of Contracts in an Inverted Index to Impression Opportunities Using Complex Predicates with Multi-Valued Attributes
In embodiments of the system 150, components of the additional content server, including modules for automated bidding management 114 and admission control and pricing module 115 perform processing such that, given an ad opportunity (e.g. an impression opportunity profile predicate), processing determines which (if any) contracts match the ad opportunity.
Herein are disclosed techniques for efficiently matching a given impression opportunity to one or more contracts. Techniques disclosed hereinabove include retrieving contracts matching a given impression opportunity from an inverted index when given conjunctions (see the Counting Algorithm and the WAND Algorithm). The intuition behind these algorithms is to efficiently eliminate contract evaluation for matching attribute-value pairs based on the count of the number of matching attribute-value pairs for a given conjunction. For instance, the impression opportunity predicate (state IN {CA,AZ} AND age IN {r3, r4}) has conjunct size of 2. This means that during impression opportunity query evaluation, only contracts that contain two or fewer conjunctions need be evaluated. The Counting Algorithm and the WAND Algorithm (and variants) are well suited to efficient retrieval of contracts where each and every impression opportunity query conjunction specifies only one value, such state=CA AND age=r5.
However, if even one of the impression opportunity query conjunction specifies an attribute that is multi-valued, e.g. state IN {CA,AZ}, simply counting the number of matches can generate invalid results. For instance, contract cZ(state IN {CA,AZ} AND age IN {r3, r4}) has conjunct size 2 and it would have two matches for query state IN {CA,AZ} AND age=r5, however contract cZ should not be returned since the age=5 attribute-value test fails. One technique for addressing this problem is to expand the multi-valued attributes into ORs. For instance, if both attributes state and age are multi-valued, as in (state IN {CA,AZ} AND age IN {r3, r4}), then the predicate would be expanded as {(state=CA AND age=r3) OR (state=CA AND age=r4) OR (state=AZ AND age=r3) OR (state=AZ AND age=r4)}. Of course, this means that if a contract has v multi-value attributes, each with v_k possible values, it would be indexed using the number of ORs in the product v—1 times v—2 times . . . times v_k. This product becomes large quickly as the number of ORs in the product increases, and thus might generate a very large index for a given multi-valued contract.
Another approach uses the inverted index construction techniques described in the Counting Algorithm and the WAND Algorithm (thus avoiding creating very large indexes for multi-valued contracts), yet efficiently retrieves contracts matching an impression opportunity profile predicate involves.
Using the inverted index construction techniques discussed above, at the time a contract is indexed, it is indexed without expansion (e.g. according to the inverted index construction techniques detailed in the WAND Algorithm).
Consider the Example Contracts listed below, for which contracts their corresponding identifiers, conjunctions, and conjunction sizes are shown in Table 30.
age ε {r3, r4}
age ε {r5}
age ε {r3, r4}
age ε {r3, r4}
gender ε {F}
The conjunctions are first partitioned according to their sizes (ec1,ec2 each have a size of 2, ec3 has a size of 1, ec4 has a size of 3, and ec5 has a size of 4). For each size partition size=1, 2, 3, 4 . . . , Table 14 shows the construction of the inverted index. The Key & UB column of Table 15 includes the shorthand representation of a key and an upper bound (UB) of weighting, and the Posting List expressions are written using the earlier-presented representation syntax.
When a multi-valued opportunity impression profile predicate is received for query against the inverted index, the multi-valued opportunity impression profile predicate is processed as follows:
For instance, given the query (state IN {CA,AZ} ^ age IN {r3,r5} ^ income=6), the following query would be created (AND (OR (state=CA, state=AZ), OR (age=r3, age=r5)), income=6). In this example income is not a multi-valued attribute. The query of this example may be represented as a two-level Boolean tree, where the first level is an AND and the second level includes one OR per multi-valued attribute (i.e. the multi-valued attributes state and age) and one leaf node for each attribute that is not multi-valued (i.e. the single-valued attribute income).
Following this solution, counting the number of occurrences under the top AND node as conjunctions produces the correct results when contracts are indexed and retrieved according to the WAND Algorithm. For instance, the reconstructed query (AND (OR (state=1, state=2), OR (age=3, age=5)), income=6) would return Example Contract EC4. This technique efficiently processes multi-valued attributes in impression opportunity profile predicates when retrieved from the above-described inverted index of contracts. Moreover, this technique does not require an index of contracts formed using expansion into constituent conjunctive normal form predicates to represent the contract's multi-valued attributes.
The propositional logic diagram 500 also shows a multi-valued query, specifically a multi-valued impression opportunity profile predicate 530. Such an expression might be formatted into conjunctive normal form predicates 540. In this case, representation as conjunctive normal form predicates results in an expansion into two AND predicates, with each of the two AND predicates having a conjunction size of 2 (see 545). As earlier indicated, reformatting using this expansion technique may result in large representations (e.g. many predicates in the expansion) as the number of multi-valued attributes and their values increases. Thus in one embodiment, preparing the multi-level representation does not include expanding the impression opportunity profile predicate into constituent conjunctive normal form predicates (which may result in a large number of conjunctive normal form predicates) and, instead, employs one or more of the herein disclosed techniques.
The propositional logic diagram 500 also shows exemplary results of the herein disclosed techniques for multi-level predicate representation. Specifically, the multi-level representation of a multi-valued impression opportunity profile predicate 550 is shown as having a first level of the multi-level representation indicating the number of impression opportunity profile predicate conjunctions. In this example, the count of the expressions at the first level (i.e. 552, 554, and 556) indicates the number of impression opportunity profile predicate conjunctions (see 555). The multi-level representation of a multi-valued impression opportunity profile predicate 550 can be further described as having a second level of the multi-level representation that represents at least one multi-valued predicate. In this example, the second level is comprised of the parenthesized OR expressions, namely 558 and 559.
In further detail,
In some embodiments, the system 150 might host a variety of modules to serve for preparing a multi-level representation of a multi-valued impression opportunity profile predicate pertinent to contract delivery methods. For example, system 150 might include an impression and contract tree construction module 116 that cooperates with any other modules of system 150 to advantageously match contracts to impression opportunities, for example the matching and projection module 117.
Section VIII: Automatic Matching of Contracts in an Inverted Index to Impression Opportunities Using Complex Predicates and Confidence Threshold Values
In embodiments of the system 150, components of the additional content server, including modules for automated bidding management 114 and admission control and pricing module 115 perform processing such that, given an ad opportunity (e.g. an impression opportunity profile predicate), processing determines which (if any) contracts matching the ad opportunity. Hereinabove are disclosed techniques for efficiently retrieving contracts matching a given impression opportunity from an inverted index when given conjunctions (see the Counting Algorithm and the WAND Algorithm). The intuition behind these algorithms is to efficiently eliminate contract evaluation for matching attribute-value pairs based on the count of the number of matching attribute-value pairs for a given conjunction. For instance, the impression opportunity predicate (state IN {CA,AZ} AND age IN {r3, r4}) has a conjunct size of 2. This means that during an impression opportunity query evaluation, only contracts that contain two or fewer conjunctions need be evaluated.
However, in some cases, the assignment of a value to an attribute may be based on statistical confidence rather than on certitude. For example, a data gathering and statistics module 112 might accurately report that there are one million drivers of imported automobiles. However such a report might have been based on a small sample population. And the sample data might only indicate which drivers are male and which are female within a statistically accurate +/−20% margin of error. Thus the data might be reported as driverimported=“male” {confidence 30%} and/or driverimported=“female” {confidence 30%}. Given that the certainty of a data point in a multi-dimensional space may be qualified with a confidence measure, it follows that a contract might express permittivity for matching impressions. In the context of advertising contracts, an advertiser might seek a target that is codified by either a single-value attribute predicate or multi-value attribute predicate (i.e. as described above). However, such a predicate (e.g. {state=California} might be more specific than desired by an advertiser based on the border of California and Arizona. For example, an advertiser based in California might be inclined to dedicate advertising resources to reach targets who are in Arizona—so long as there is a high likelihood (as defined by the advertiser) that the target meets other demographic criteria.
As just described, a confidence value may be defined by an advertiser in order to codify acceptable permittivity into a targeted advertising campaign. Of course the characterization of an impression opportunity profile may be subject to uncertainty or statistical variance. For example, characterization of a particular user corresponding to an impression opportunity profile might include an attribute for an educational degree (e.g. B.A., B.S., M.S.E.E., Ph.D., etc). In the case that the user's degree status was retrieved from the database of an accredited institution of higher learning, the confidence might be relatively high. Conversely, in the case that the user's degree status was retrieved from a social networking site, the confidence might be relatively lower. A data gathering and statistics module 112 might report that a particular user is domiciled in California with a 95% confidence, but only a 50% confidence the user is domiciled in San Francisco, Calif. Accordingly techniques are herein disclosed for efficiently retrieving matching contracts where matching includes matching based on both the predicates and also the confidence corresponding to the predicates.
One approach extends the inverted index construction techniques described in the Counting Algorithm and the WAND Algorithm to add confidence measures to the inverted index data structure while preserving the efficiency in retrieving contracts matching an impression opportunity profile predicate.
Consider the Example Contracts listed below, for which contracts their corresponding identifiers and predicates are shown in Table 16.
(state ε {CA} {50%}
For impression I1: (gender=M{75%}, state=AZ{50%}, state=CA {60%}, state=AK{74%}), evaluation of the impression I1 against the contracts of Table 16, contract ec6 would be a valid match while ec7 would not be a match. Embodiments of the invention extend the inverted index construction techniques described in the Counting Algorithm and the WAND Algorithm to add confidence measures to the inverted index data structure while preserving the efficiency in retrieving contracts matching an impression opportunity profile predicate. In one embodiment, confidence values are stored in the inverted index along with the contract identification in a posting list for a particular predicate.
Embodiments of the invention define one or more query evaluation operators. For example, a query operator might be described as IN_THRESHOLD. In this embodiment, the IN_THRESHOLD operator takes as input parameters: (a) a contract C with contract C having confidence values included in the herein-described inverted index, and C having a set of predicates P with confidence values V; (b) an impression query Q having a set of predicates with confidence values J; and (c) a function F.
The operator IN_THESHOLD(C, Q, F) evaluates to TRUE if and only if:
For instance, consider the two contracts of Table 16 and impression (gender=M{75%}, state=AZ{50%}, state=CA{60}, state=AK{74%}), and if F=sum (i.e. the arithmetic operator sum), then:
As described, if C is a valid contract for impression Q without considering the confidence values, then only one of the arithmetic thresholds corresponding to a contract predicate need be satisfied by the impression in order for the operator IN_THESHOLD(C, Q, F) to be satisfied.
Again consider the two contracts of Table 16 and impression I2: (gender=M{50%)}, state=AZ{60%}, state===CA {60%}, state=AK{74%}), and if F=sum (i.e. the arithmetic operator sum), then:
As another example, consider the two contracts of Table 16 and impression I3: (gender=M{50%}, state=AZ{59%}, state=CA {49%}), and if F=sum (i.e. the arithmetic operator sum), then:
In various embodiments of the invention, the operator IN_THRESHOLD can be efficiently implemented using an inverted index. More specifically, a threshold value for a contract term may be represented in the index as a literal numeric value, or as a numeric value accessed through one or more levels of indirection. In some embodiments, a threshold value is represented as an integer between zero and 100 (i.e. representing a percentage), or as a real number between 0.0 and 1.0 (i.e. representing a percentage), or as any other representation that can yield the value of a percent.
Using an inverted index as shown and described in
For each remaining contract, evaluate F.
For example, given the impression I4: (gender=M {75%}, state=AZ{50%}, state=CA {60%}, state=AK {76%}), and if F=sum (i.e. the arithmetic operator sum), then:
For each remaining contract (since ec6 and ec7) evaluate F over the predicates:
The correspondence of a confidence value may be noted using the bracket notation where confidence values are represented as percentages within brackets appended to a predicate (e.g. state=AK{74%}). In an alternative notation, the correspondence of a confidence value may be noted using the bracket notation where confidence values are represented as percentages within brackets appended to the posting list contract identification (as shown in
Processing IN_THRESHOLD for Arbitrarily Complex Boolean Expressions:
The operator IN_THRESHOLD may be efficiently processed in the context of more complex Boolean expressions. In particular, and as disclosed herein, an arbitrarily complex expression may be represented as an AND/OR tree, having the highest level branches representing conjunctions for processing using the Counting Algorithm or the WAND Algorithm or variants. This means that it can be combined with other operators in the context of larger Boolean expressions.
Section IX: Automatic Matching of Contracts Using a Fixed-Length Complex Predicate Representation
As earlier disclosed in the discussion of system 150, in the case of online Internet advertising, an item of inventory (e.g. an impression) might be specified in an arbitrarily complex description that might involve dozens, or hundreds or even more attributes and values, which attributes and values are to be matched to one or more matching contracts. A system 150 may be configured to include an ad server and admission control module in order to answer the following fundamental question: “Given an ad opportunity, what are the contracts matching it?” Hereinabove is disclosure of how to build such an index when opportunities are specified by arbitrarily complex contracts (e.g. stored as arbitrarily complex Boolean expressions) without converting the contracts to CNF or DNF. This allows for both faster retrieval (due to quicker evaluation of contracts), while at the same time having lower space usage. Some retrieval techniques include use of a numbering scheme to represent nodes in this tree whereby the numbers representing the nodes are variable length. Retrieval using variable length node representations may include interpretation (i.e. a processing-intensive step) of the variable length number. Moreover, the selection of certain characteristics of the numbering scheme imposes corresponding limitations. In some cases, the use of a variable length numbering scheme imposes limits on the height of the tree and/or on the maximum number of children allowed by any node. As the number of predicates upon which to match increases, processing involving variable number interpretation in retrieval operations also increases. Thus, in embodiments of the current invention, techniques for indexing arbitrarily complex Boolean expressions based a fixed length representation for each node in the tree are used. Moreover, the retrieval techniques disclosed herein support retrieval of all contracts that satisfy the predicates of an opportunity. That is, given an impression opportunity A specified as a vector V of (feature, value) pairs, the retrieval techniques disclosed herein may be configured to return all of the contracts that match this opportunity. For example, given an impression opportunity profile specified as a vector of feature-value pairs, the impression opportunity A0 {(state=IN{CA,AZ} AND age IN {r4, r4} AND income=6}, possible matching contracts are any of those contracts asking for users from CA or AZ, contracts asking for users in age range r3 or age range r4 AND income=6.
Using the techniques herein, contracts expressed as arbitrarily complex Boolean expressions can be handled efficiently without converting to much larger CNF or DNF formulas.
As shown in step 1240, for each contract selected, construct an AND/OR contract tree representation and label each node from 1 to M. Evaluate only leaf node contract predicates to TRUE/FALSE as evaluated against the leaf node impression predicates of the impression tree (see step 1250). That is, for each contract tree leaf node contract predicate, compare the required predicate (e.g. gender IN(Male)) against the impression tree leaf node impression predicate for satisfaction (i.e. TRUE or FALSE), and mark the corresponding tree leaf node contract predicate (e.g. as TRUE). In some embodiments, including computer-implemented embodiments, the initial set of contract tree leaf node data structures are initialized to a FALSE value, and subsequently marked as TRUE when the evaluation against a corresponding impression tree leaf node predicate is determined to be TRUE.
The operations of step 1260 are for projecting (using the marked contract tree leaf node predicates) the label assigned to the marked contract tree leaf node predicates over a discrete set of ordered symbols (e.g. discrete series of integers on order from 1 to M). Various methods (e.g. list mapping, set operations, etc) are suited to project the TRUE nodes into a discrete series of integers from 1 to M (see step 1260). The operations of step 1270 check for a contiguous projection from 1 to M over the discrete series of integers from 1 to M, and return contracts where the projection yields a contiguous projection from 1 to M (see step 1270).
In this embodiment of the invention, the discrete series of integers from 1 to M is a particular species of the genus of a discrete set of ordered symbols. Use of integers is purely illustrative, and any discrete set of symbols that can be arranged into an order may be used. Moreover, representation of an integer or symbol need not be limited to a computer-implemented integer. A symbol might be represented as an element in a set, or even as a series of bits within a computer memory. It should be noted that some of the examples herein use a discrete set comprised of decimal (base 10) representations of integers from 1 through 15, plus the symbol M, which is ordered contiguously as {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, M}. It should further be noted that the discrete set over which the set of TRUE conjuncts is projected need not be the same discrete set between contracts. In fact, and as described herein as pertains to some embodiments, each contract selected in method step 1230 might be returned together with an annotation of a pair of {start, end} numbers describing its position in the inverted index, and that pair of {start, end} numbers might be used to select the lower and upper bounds of the aforementioned discrete set (e.g. using integer portions from the pair of {start, end} numbers, with all integers in between).
Now, using a sample case, the following paragraphs illustrate application of step 1210 through step 1270 as applied to the sample case of Table 17. Consider the following impression (and note the use of confidence measures and multi-valued IN predicates):
gender IN {Male}
topic IN {Life}
topic IN {News}
geo IN {Santa Clara {60%}}
geo IN {New York {99%}}
income IN {50 k-100 k}
clickHistory IN {Active}
Such a list of lowest-level predicates are then used to query and retrieve from an inverted index (possibly using the conjunct-oriented retrieval techniques discussed above) contracts that have as a term any one of the lowest-level predicates (see step 1230). The set of contracts returned may include contracts that are not satisfied against the entire complex predicate of the impression, however techniques for identifying contracts that do satisfy the complex predicate of the impression are discussed infra.
In some embodiments, each contract selected in method step 1230 is returned together with an annotation of a pair of {start, end} numbers describing its position in the inverted index.
In the examples of
Details of Step #1 and Step #2 of Algorithm 4 are further described in the following Algorithm 5.
One may observe that the sum label at any node is equal to the number of leafs (conjuncts) represented by that node. In this example, and in the representation as shown, the entire predicate expands to 16 conjuncts.
1: geo IN_THRESHOLD {Santa. Clara, Sunnyvale} {Confidence 50%}
2: geo IN_THRESHOLD {Palo Alto} {Confidence 60%}
3: geo IN_THRESHOLD (California) {Confidence 70%}
4: geo IN_THRESHOLD (West Coast) {Confidence 90%}
5: geo IN_THRESHOLD {New York) {Confidence 98%}
6: gender IN {Male}
7: topic NOT_IN {Sports, Finance}
8: topic IN {Life, Mortgage}
9: gender IN {Female}
10: topic NOT IN {Entertainment}
11: gender IN {Male, Female, Unknown}
12: topic IN_THRESHOLD (Banking) {Confidence 95%}
13: income IN {100 k-200 k, above 200K}
14: income IN {50 k-100 k}
15: clickHistory IN {Active}
16: clickHistory IN {Very Active}
Next, the details of the algorithm corresponding to Step #4 of Algorithm 4 (i.e. for assigning the {begin, end} using n.begin, and n.end values) are presented in Algorithm 6, below. Once a tree has been labeled according to Algorithm 6, the labeled tree exhibits the following characteristics:
A contract can be conceptualized as a set of discrete line segments from {0, 1, 2, . . . M}, where M is some maximum constant (e.g. 255). Each discrete line segment can be represented as a sequence of consecutive integers N0 through NM, where Ni+1=Ni+1, and NM is at most M. Each leaf node of the contract as represented in the form of
Many algorithms might be employed to accomplish the aforementioned projection. One such algorithm is presented below as Algorithm 7. Algorithm 7 is suited for implementation on a general purpose computer.
Again referring to
The projection of the TRUE conjuncts onto a discrete number line can be narrated as follows: Allocate a data structure Frontier to be a data structure for representing a discrete contiguous number line segment (i.e. a possible implementation of a discrete ordered set). Initialize Frontier to {0}. This data structure Frontier is initialized as {0} and for each conjunct being evaluated, a TRUE evaluation results in adding the segments (i.e. segments that are projected by a TRUE evaluation of a conjunct) to the Frontier data structure. For example, Table 18 below shows a running example based on the projection-annotated contract tree 1600 being evaluated against the sample impression of Table 17:
Note that even though 5 conjuncts (leaf nodes) are evaluated to TRUE (see the bolded leaf nodes and their projections), the sample impression and the sample contract are deemed to match. Those skilled in the art will recognize that it is not always necessary to evaluate all nodes in a projection tree, i.e. evaluation processing may stop when it is known that the projection of the evaluated conjuncts projects over the entire discrete symbol set.
As can be seen, this technique solves the problem indexing arbitrary Boolean expressions for efficient evaluation, yet overcomes size factors that become limiting as the size of Boolean expressions to be indexed increases. For instance, using this technique, and using just two bytes to represent each {begin, end} pair, Boolean trees with up to 256 leaf nodes can be indexed. Using four bytes to represent each {begin, end} pair, Boolean trees with up to 64 k (i.e. 28−1) leaf nodes can be indexed. Moreover, this technique may be practiced using a very efficient evaluation algorithm that does not require the interpretation of Dewey ids.
In some embodiments, a system 150 might host a variety of modules to serve for automatic matching of contracts using a fixed-length complex predicate representation. For example, system 150 might include an impression and contract tree construction module 116 that cooperates with any other modules of system 150 to advantageously matching contracts using a fixed-length complex predicate representation, for example the matching and projection module 117.
Section X: Detailed Description of Exemplary Embodiments
As used in the subject disclosure, the terms “annotate”, “annotating”, “label”, “labeling”, “mark”, and “marking” all refer to the same concept of identifying an object as having a particular attribute. While the term “annotate” is convenient when discussing figures printed on pages, an art-specific term such as “marking” may be more convenient in discussion within the arts related to computer-implemented methods. As used in the subject disclosure, the terms “component” “system”, “module”, “processor”, “memory” and the like are intended to refer to a computer-related entity, either hardware, software, software in execution, firmware, middleware, microcode, and/or any combination thereof. For example, a module can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, a device, and/or a computer. One or more modules can reside within a process and/or thread of execution and a module can be localized on one electronic device and/or distributed between two or more electronic devices. Further, these modules can execute from various computer-readable media having various data structures stored thereon. The modules can communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g. data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems by way of the signal). Additionally, components or modules of systems described herein can be rearranged and/or complemented by additional components/modules/systems in order to facilitate achieving the various aspects, goals, advantages, etc. described with regard thereto, and are not limited to the precise configurations set forth in a given figure, as will be appreciated by one skilled in the art.
Any node of the network 2300 may comprise a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof capable to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices (e.g. a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration, etc).
In alternative embodiments, a node may comprise a machine in the form of a virtual machine (VM), a virtual server, a virtual client, a virtual desktop, a virtual volume, a network router, a network switch, a network bridge, a personal digital assistant (PDA), a cellular telephone, a web appliance, or any machine capable of executing a sequence of instructions that specify actions to be taken by that machine. Any node of the network may communicate cooperatively with another node on the network. In some embodiments, any node of the network may communicate cooperatively with every other node of the network. Further, any node or group of nodes on the network may comprise one or more computer systems (e.g. a client computer system, a server computer system) and/or may comprise one or more embedded computer systems, a massively parallel computer system, and/or a cloud computer system.
The computer system 2350 includes a processor 2308 (e.g. a processor core, a microprocessor, a computing device, etc), a main memory 2310 and a static memory 2312, which communicate with each other via a bus 2314. The machine 2350 may further include a display unit 2316 that may comprise a touch-screen, or a liquid crystal display (LCD), or a light emitting diode (LED) display, or a cathode ray tube (CRT). As shown, the computer system 2350 also includes a human input/output (I/O) device 2318 (e.g. a keyboard, an alphanumeric keypad, etc), a pointing device 2320 (e.g. a mouse, a touch screen, etc), a drive unit 2322 (e.g. a disk drive unit, a CD/DVD drive, a tangible computer readable removable media drive, an SSD storage device, etc), a signal generation device 2328 (e.g. a speaker, an audio output, etc), and a network interface device 2330 (e.g. an Ethernet interface, a wired network interface, a wireless network interface, a propagated signal interface, etc).
The drive unit 2322 includes a machine-readable medium 2324 on which is stored a set of instructions (i.e. software, firmware, middleware, etc) 2326 embodying any one, or all, of the methodologies described above. The set of instructions 2326 is also shown to reside, completely or at least partially, within the main memory 2310 and/or within the processor 2308. The set of instructions 2326 may further be transmitted or received via the network interface device 2330 over the network bus 2314.
It is to be understood that embodiments of this invention may be used as, or to support, a set of instructions executed upon some form of processing core (such as the CPU of a computer) or otherwise implemented or realized upon or within a machine- or computer-readable medium. A machine-readable medium includes any mechanism for storing or transmitting information in a form readable by a machine (e.g. a computer). For example, a machine-readable medium includes read-only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical or acoustical or any other type of media suitable for storing information.
While the invention has been described with reference to numerous specific details, one of ordinary skill in the art will recognize that the invention can be embodied in other specific forms without departing from the spirit of the invention. Thus, one of ordinary skill in the art would understand that the invention is not to be limited by the foregoing illustrative details, but rather is to be defined by the appended claims.
| Number | Name | Date | Kind |
|---|---|---|---|
| 20100042930 | Pritchard et al. | Feb 2010 | A1 |
| 20100257054 | Martin et al. | Oct 2010 | A1 |
| Number | Date | Country | |
|---|---|---|---|
| 20110213767 A1 | Sep 2011 | US |
