Patent Application
20130085821
Refer-a-friend programs seek to motivate participants in a group to encourage their acquaintances to join the group. When the benefit of each new member exceeds the cost of motivating the referral, refer-a-friend programs are inherently beneficial to the agency running the group. However, in some scenarios, the benefit of each new member may vary, and this benefit might not even be known at the time of the referral. Thus, a conventional refer-a-friend program might yield referrals that have an unfavorable cost/benefit trade-off to the agency. In addition, if a participant's benefit from a referral is greater than the base cost of joining the group, a participant might strategically create multiple identities, send a referral to each identity, and have each identity join the group, thereby gaining value at the expense of the agency. Such multiple-identity attacks are particularly problematic for online systems, where forged identities are easy to create and difficult to detect. Furthermore, conventional referral programs only provide incentive for direct referrals; they do not incentivize a participant to refer other participants who might then refer other participants. Thus, even though referring further referrers is beneficial to the group, this behavior is not encouraged by these types of programs.
This disclosure describes techniques for incentivizing and rewarding participants of a participatory system such as an online service or web site in which participants solicit or refer other participants. The techniques involve identifying participant subsets that include the participant and any descendant referrals of the participant. A participant's effective contribution is then identified based on the collective contributions of the participant's subset. Based on the participant's effective contribution, a weighted net contribution of the participant is calculated and rewards are distributed deterministically according to the weighted net contributions of the individual participants. The weighted net contribution is calculated in such a way that the size of the reward to any given subset of participants is determined by the quantified contributions of those participants.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The detailed description is set forth with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.
The web service 102 may also comprise a service that accepts contributions of various types from participants, such as various types of crowd-sourcing services. For example, the web service may be a knowledge base to which users contribute, or a public service site that seeks to offload small computing tasks to willing participants.
Various other services and programs may also utilize the techniques described herein, including subscription services, membership programs, and other programs in which participants pay to utilize services.
In order to promote and encourage participation in the web service 102, the web service may offer rewards. Rewards may include anything that potential participants perceive as having tangible or intangible value, such as financial compensation, credits, monetary payments, points, badges, titles, goods, services, gifts, donations to charitable causes, ownership shares, revenue shares, and so forth. The sizes of the rewards are determined in a fashion that is designed to encourage user participation, and to also encourage referrals of other participating users. Furthermore, the sizes of rewards for individual participants are calculated in such a way as to resist strategies that might otherwise be used to manipulate or “game” the system to gain undeserved advantages or rewards.
The participatory web service 102 may have a primary services component 106 that provides or implements the primary services and/or functionality of the web service 102. The primary services component 106 may comprise one or more servers and associated infrastructure, customized for the particular functionality of the web service 102.
The participatory web service 102 may also include membership and reward distribution components or functionality 108 for keeping track of users, their contributions, and their referrals. The membership and reward distribution components may include a referral or relationship tracking component 110 and a contribution logging component 112. The relationship tracking component 110 allows users to refer other users, and keeps track of which users have referred which other users. The contribution logging component 112 quantifies and keeps track of contributions by individual users.
Contributions by individual participants may be quantified in terms various parameters, such as page views, numbers of submitted searches, contributed CPU cycles, words contributed, and so forth. Contributions may be in the form of actual submissions or services. Contribution may alternatively be measured as the quantity of services consumed by the participant from the web service 102, or the amount the services are used by the participant. The terms “participation” and “contribution” will be used interchangeably in the following discussion to indicate a quantified measure of desired participant behavior.
The left side of
The relationships between participants form various participant subtrees or subsets, with each subset having a single head. For example, a subset 116 is formed by the participants within the dotted oval. The participant Carol is at the head of the subset 116, and the subset 116 may be referred to as Carol's subset. The subset 116 includes Carol, Dave, Ellen, Frank, Ginny, Harold, Irene, and Jim. As another example, a subset is formed by Ginny, Irene, and Jim, with Ginny at the head. Each participant has a corresponding subset, of which he or she is the head. Participants other than the head are described as descendants of the head participant.
Formally, a participant's subset is the set of participants containing the participant, all other participants referred by the participant, all participants referred by those participants, and so on, such that each participant subset includes all participants referred by all members of the subset. In some contexts, a referring participant might be referred to as a parent, and those referred by the parent might be referred to as children of the parent.
When a new person joins the service 102, they may do so as either an original user or a referred user. For example, people might sign up to the service 102 by visiting a web site that records information or installs an application. If someone visits the site on their own, they join as a child of the system root node (not shown) that represents the executive or administrative entity associated with the service 102. Once a member, a participant is able to send solicitations, perhaps in the form of coded email links, to friends and associates. Anyone who follows the coded link to the web site will join as a child of the participant who sent the link.
In practice, solicitations may occur in numerous ways. For instance, participant v can send a recommendation email to friend w containing a link that leads to a sign-up page. When clicking on this link and signing up as a new participant of the networked system, the URL indicates v as the solicitor of w. In another case, at the time of joining the network, a new participant may indicate its solicitor by providing its (e.g., the solicitor's) name or identification number (e.g., systems with off-line sign-up procedure). In this case, w would list v as w's solicitor when completing a sign-up procedure. In still another example, depending on the nature of the service 102, it is also conceivable that the solicitor of a specific node changes during the course of the system's lifetime.
As mentioned above, each member's contribution can be monitored, measured, and quantified. Referring again to
Contribution can be measured utilizing one or more criteria. An example of such criteria can include an amount of computing time (e.g. in CPU cycles) offered to a distributed computing or grid computing project. Another example can be the number of software updates/products installed from a software company. A further example can be the amount, number, or frequency of services purchased and/or consumed from the service 102, such as the number of keyword searches performed or the number of other service features used. A further example can be the number of recommendations submitted to a networked recommendation system. Another example can be the number of pictures tagged with keywords in a networked system that classifies picture-databases. A further example can be the amount of storage provided to a distributed, decentralized file-storage system. Still another example can be the number of games played on an online game site or in a massive multiplayer games setting. A still further example can be the number of files or blocks uploaded to a P2P system. The skilled artisan should recognize still other examples.
Regardless of what exactly constitutes contribution in any given system, exemplary implementations can formally express the contribution as a number that becomes larger as the participation in and/or contribution to the system gets larger.
In a system without measurable contribution, or when soliciting new participants constitutes the primary contribution (as in certain marketing schemes or social networks), the contribution can also be set to be uniform (e.g., equal to 1) over all participants in the participant/referral tree.
As mentioned above, there may be many original participants who have not been referred by other participants. For purposes of discussion and analysis, these participants are considered to be children of a single parent or root node corresponding to the executive entity of the web service 102.
An action 302 comprises identifying and/or tracking the current participant, contributions by the current participant, and referrals made by the current participant, thereby allowing the logical representation of a participant base in a directed graph such as that illustrated in
An action 304 comprises identifying a participant subset corresponding to the current participant. As mentioned above, the current participant's subset includes the current participant and all descendents of the current participant. Stated alternatively, a participant's subset includes the participant and all other participants referred by any members of the subset.
An action 306 comprises evaluating and/or quantifying the contribution of the participant's subset. In the described embodiment, this comprises quantifying the contribution of each participant in the current participant's subset.
An action 308 comprises calculating the weighted net contribution of the current participant. This action is subject to a property 310 that is referred to herein as the property of strict contribution determinism: for any subset of participants that includes all participants referred by members of the subset, the collective reward received by all participants in the subset is determined strictly by the contributions of those participants. A specific example of a technique for calculating weighted net contribution will be described below, with reference to
An action 312 comprises distributing awards to participants, where the sizes or portions of the rewards are calculated deterministically, based on a total available reward 314 and on the calculated weighted net contribution of each participant. In certain embodiments, the size of the total reward 314 for any particular participant is proportional to the participant's weighted net contribution. In some embodiments, the reward for a participant may be calculated by multiplying the total reward 314 by the participant's weighted net contribution. A participant's reward includes portions for both direct contributions and for contributions by referred descendants of the participant.
As will be described in more detail below, the action 308 of calculating a participant's weighted net contribution may be based on a convex function π(c). An exponential function is an example of a convex function. In the described embodiment, the function comprises a square function: π(c)=c2. This particular function has the properties that an input of zero yields an output of zero, and an input of one yields an output of one. These two properties are not necessary, but they simplify the use of the function.
Generally, the weighted net contribution of a participant can be calculated as follows. A weight or weighted contribution W(n)=π(C(n)/C(Sys)), is computed for each node n in a participant/referral tree, where C(n) comprises the effective contribution of the node n, and C(Sys) is the summed contribution of all nodes. Thus, W(n) is the function π(c) applied to the node n's proportional contribution. For example, if the sum of all system contributions is 40 and a participant n's effective contribution is 5, then n's proportional or fractional contribution is 5/40=⅛ and its weighted contribution W(n) is W(n)=π(⅛). Stated alternatively, the weight or weighted contribution W(S) of any subset S is defined as π(c) applied to this subset's proportional contribution. For example, the weight of a subset S consisting of three nodes with direct contributions 3, 4, and 7 is W(S)=π((3+4+7)/40)=π(7/20).
An expected value or weighted net contribution L(n) is then calculated for each node n. L(n) is calculated as the weight of the subset rooted at n minus the weights of subsets corresponding to all children of n.
At 410, the effective contribution 408 of the participant is divided by the summed contributions 412 of multiple participants, which will typically comprise all participants of the service 102. This yields the fractional or proportional effective contribution 414 of the participant. Note that the action 414 may be omitted in certain embodiments, and subsequent actions may act upon the effective contribution 408 rather than on the fractional effective contribution 414.
At 416, the convex function π(c) is applied to the effective contribution 408 or to the fractional effective contribution 414 to yield the weight or weighted contribution 418 of the participant.
At 420, the weighted contributions 422 of any child participants of the current participant are subtracted from the participant's weighted contribution 418 to yield the net weight or weighted net contribution 424 of the participant.
As described above, the size of the reward to an individual participant may be based on the weighted net contribution of the participant, and may in certain embodiments be proportional to the participant's weighted net contribution. Because of the way the weighted net contribution is calculated, rewards are granted based primarily on the contributions of a system's users, avoiding the situation in which rewards might be granted for referrals that end up exhibiting no beneficial contribution. Rather, a participant is rewarded based on the amount of contribution of referred users. Also, a participant is rewarded for referring participants who refer other participants—assuming that all of the participants actually contribute.
Furthermore, because of the property of strict contribution determinism exhibited by the technique of
Additionally, the described techniques do not provide an incentive for participants to bypass referrers. The concern here is that prospective participants might react to referrals by ignoring the referrer and instead joining the system as unreferred participants. If this were the case, participants might become reluctant to refer, because they might assume that people will join as unreferred participants and thus not provide any reward to the referrer. However, the property of strict contribution determinism is sufficient to prevent participants from benefitting by bypassing their referrers.
Beginning at block 506, this implementation executes a post-order traversal of the participant/referral tree. That is, all nodes are visited one by one recursively in the order left-most subset, second left-most subset, . . . , right-most subset, node. At each step of this traversal in which a node n is considered, the weight of n's subset W(T1) and the weighted net contribution L(n) are computed.
At block 508 for a node n, the technique computes the total contribution C(Tn) of n's subset Tn. Note that due to the post-order traversal, all values C(m) for every descendent m of n are already known to the algorithm. At block 510, the technique computes the weighted contribution W(Tn) of n's subset by applying the function π(c) to the ratio C(Tn)/C(Sys), where C(Sys) denotes the total contribution in the system, i.e., W(Tn)=π(C(Tn)/C(Sys)).
At block 512 the technique computes the weighted net contribution L(n) of n by taking the subset weight W(Tn) and subtracting from it the weighted contributions of all child participants of the current participant. This can be expressed as:
L(n)=W(Tn)−Σall children m of n W(Tm)
If a node has no children, its weighted net contribution L(n) is simply its weighted contribution; that is, L(n)=W(n).
Note that due to the post-order traversal, all values W(Tm) are already known to the algorithm.
At 514 the technique queries whether ncur is the root of the solicitation tree. In an instance where ncur is not the root (i.e., “no” at block 514), then the process proceeds to block 516 to continue the post-order traversal. In an instance where the ncur is the root (i.e., “yes” at block 514) then the technique returns the calculated weighted net contribution of the individual participants to the method of FIG. 3—reaching the root indicates that the process is complete due to the nature of the post-order traversal.
At block 516, the technique proceeds by letting ncur be the next node in the post-order traversal of Tsys. From block 516, the technique returns to block 508. Accordingly, blocks 508-516 can be repeated until the traversal of the entire tree is complete, and the weighted net contribution of every participant has been calculated. The results can be returned to the process illustrated by
Note that the illustrated method 500 can be implemented as a recursive function that is called first for the root, and which recursively calls itself for calculations regarding descendent subsets.
The first node to be computed is designated generally at 602 and has a contribution of 12. This node's weight is π(12/40)=0.09 and because this node has no children, this is also its weighted net contribution L(n). So in this case, the node is designated with “12:0.09”, indicating its direct contribution and its weight. The next node to be considered in the post-order traversal is the left-most leaf-node with contribution 2 designated generally at 604. This node's weight is π(2/40)=0.0025 and again because it does not have any children of its own, this also corresponds to this node's weighted net contribution. The same also holds for its two siblings designated at 806, 808 that also have a contribution of 2. These two nodes are the next to be considered in the post-order traversal. Finally, the fifth step of the post-order traversal considers the node with contribution 8 designated at 610. Doing the same calculation as above reveals that this node 610 has a weighted contribution of L(n)=π(8/40)=0.04.
L(n)=0.16−0.0025−0.0025−0.0025−0.04=0.1125
L(n)=0.5625−0.09−0.16=0.3125
Note that the root 908, which corresponds to the executive entity, will be assigned a net weight when using this procedure. The root 908 has no actual contribution, but has a calculated weighted net contribution of 0.405 due to its attributed referrals. For purposes of calculating reward amounts, the root's weighted net contribution may be ignored.
It certain situations it might be beneficial to convey to participants information about what part of their rewards came from their direct contribution versus what part came from contributions by their referrals. The participant's non-referral reward can be calculated as the participant's weighted contribution multiplied by the total reward for all participants. The participant's referral reward can be defined as the participant's total reward minus the participant's non-referral reward.
The computer 500 may comprise one or more processors 1002 and computer-readable memory 1004. The techniques described above may be implemented as software residing within the computer-readable memory 1004, such as one or more programs, modules, or routines, comprising sets or sequences of instructions that reside in the memory 1004 for execution by the one or more processors 1002.
The memory 1004 is an example of computer-readable media. Computer-readable media includes at least two types of computer-readable media, namely computer storage media and communications media.
Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, phase change memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device.
In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism. As defined herein, computer storage media does not include communication media.
The computer 1000 may also have input/output facilities 1006 such as network interfaces, user interfaces, and so forth.
Software used to implement the techniques described above may reside in the memory 1004 as shown, and/or may also be stored and distributed in various ways and using different means, such as by storage on different types of computer-readable memory 1004, including portable and removable media. The software may also be distributed by transmission from a repository, using a data network or other types of data computer communication systems.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
