Patent Application
20100257023
This invention relates generally to social networking and, in particular, to targeting advertising to users of a social network.
Social networks, or social utilities that track and enable connections between members (including people, businesses, and other entities), have become prevalent in recent years. In particular, social networking websites allow members to communicate more efficiently information that is relevant to their friends or other connections in the social network. Social networks typically incorporate a system for maintaining connections among members in the social network and links to content that is likely to be relevant to the members. Social networks also collect and maintain information about the members of the social network. This information may be static, such as geographic location, employer, job type, age, music preferences, interests, and a variety of other attributes, or it may be dynamic, such as tracking a member's actions within the social network. This information about the members can then be used to target information delivery so that information more likely to be of particular interest to a member can be communicated to that member.
Advertisers have attempted to leverage this information about members of social networks to target ads to members whose interests align with the ads. For example, a social networking website may display a banner ad for a concert to members who have listed an interest for the performing band on their member profile and live near the concert venue. One drawback of this type of ad targeting, however, is that it relies on the information provided by or otherwise obtained about members of the social network. Members of social networks often do not populate their profiles to include all of their interests and other personal information. As a result, using personal information in ad targeting is typically not available for all members of the social network. Traditional ad targeting techniques are thus limited because they can reach only a subset of the members in the social network for whom the ads are intended.
To optimize the targeting and selection of ads for members of a social network, embodiments of the invention leverage information in the social network to infer interests about members of the social network. A social network may maintain a social graph that identifies the mapping of connections among the members of a social network, and the social network may also maintain profiles that contain full or partial information about each of the members in the social network. One or more advertisements, or ads, available to the social network may contain targeting criteria for determining whether the ad should be targeted to a particular member. While the social network may have sufficient information about some of its members to apply the targeting criteria, the social network may not have sufficient information about other members to apply the targeting criteria. Rather than missing out on the opportunity to target ads to this latter group of members, embodiments of the invention use the information for other members to whom a particular member is connected when the social network does not have sufficient information to apply the targeting criteria to the member. This may be thought of as “inferential” ad targeting because a member's likely interest in a particular ad is inferred based on whether that member's connections (e.g., friends in the social network) are good candidates for the ad based on its targeting criteria.
Embodiments of the invention may employ various targeting criteria and methods of leveraging information in the social network to infer a member's interests based on an advertiser's campaign strategy. A simple ad targeting strategy may use targeting criteria for an ad that evaluates a particular parameter or field in a member's profile. More complex strategies may include targeting criteria that evaluates a function of the member's actions on the social network, such as the member's browsing habits. Additionally, information in the social network may be leveraged in many different ways to infer the interests of a member. Moreover, embodiments of the invention may apply the same targeting criteria to a member's connections that were applied to the member's profile that lacked information, or different criteria may be evaluated when looking to the member's connections. For example, to account for the lower level of certainly when the targeting is inferred, stricter targeting criteria may be applied to the member's connections than the targeting criteria applied to the member's profile.
Ads that have targeting criteria to be applied to a member's connections in the social network, in embodiments of the invention, may be referred to as “inferential” ads. Inferential ads may differ in the scope of inference by varying the quantity and quality of connections included in the ad targeting process. For example, secondary inferential targeting criteria may include all of the member's connections in an attempt to infer an interest for the member, or an ad may focus on a smaller subset of the member's connections. The smaller subset of member's connections may be selected because of the member's affinity for those members, or because the smaller subset share a characteristic that the advertiser wishes to target, such as being alumni of the same college. The quality or affinity associated with connections also may be varied to include multiple tiers of connections. An inferential ad may include only the member's direct connections or may include indirect connections, or the direct connections of the member's connections.
Inferential ads may also include the ability to set thresholds for targeting criteria as applied to a member's connections. For example, an advertiser may determine that an ad may infer an interest for a member if more than 25% of the member's connections satisfy the secondary inferential targeting criteria or if at least 3 connections meet the main targeting criteria, or a combination of both. The ad targeting method may also weight the member's connections or otherwise take into account the member's affinity or other measure of closeness to the member's connections. Any combination of the above methods may be implemented in the ad targeting method.
In one embodiment, the ad targeting techniques are used to determine a candidate set of ads for a member, and one or more of the ads are selected according to the revenue they are expected to generate. In another embodiment, ads are selected according to the member's affinities for the connections or another measure of the closeness of the member to the connections whose interests are inferred. In yet another embodiment, the method learns over time the affinities and interests of a member presented with inferential ads in response to their feedback. In an alternative embodiment of inferential ad targeting may be implemented regardless of whether the member's profile lacks information to satisfy targeting criteria. In other alternative embodiments, various combinations of the above inferential ad targeting techniques are implemented.
The figures depict various embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.
A social networking website offers its members the ability to communicate and interact with other members of the social network. In use, members join the social network and then add connections to a number of other members to whom they desire to be connected. As used herein, the term “friend” refers to any other member to whom a member has formed a connection, association, or relationship via the website. Connections may be added explicitly by a member, for example, the member selecting a particular other member to be a friend, or automatically created by the social networking site based on common characteristics of the members (e.g., members who are alumni of the same educational institution). Connections in social networks are usually in both directions, but need not be, so the terms “member” and “friend” depend on the frame of reference. For example, if Bob and Joe are both members and connected to each other in the website, Bob and Joe, both members, are also each other's friends. The connection between members may be a direct connection; however, some embodiments of a social networking website allow the connection to be indirect via one or more levels of connections. Also, the term friend need not require that members actually be friends in real life, (which would generally be the case when one of the members is a business or other entity); it simply implies a connection in the social network.
In addition to interactions with other members, the social networking website provides members with the ability to take actions on various types of items supported by the website. These items may include groups or networks (where “networks” here refer not to physical communication networks, but rather social networks of people) to which members of the website may belong, events or calendar entries in which a member might be interested, computer-based applications that a member may use via the website, and transactions that allow members to buy or sell items via the website. These are just a few examples of the items upon which a member may act on a social networking website, and many others are possible.
Advertisements on a social network attempt to leverage information a social network in order to reach a specific audience whose interests align with ads. To do so, advertisers employ targeting criteria for their ads to members of a social network. It is well known to use certain demographic data to target audiences for certain advertisements. For example, a pop music promoter for Britney might want to target advertisements towards certain age and gender demographics.
Advertisers on a social network may also target their advertisements to members who have listed particular interests on their member profiles. Each member has a profile in which he or she can list interests. For example, a classical music aficionado might list “Chopin” or “Bach” as interest. Advertisers may, in turn, target their ads towards members who have listed “Chopin” as an interest. A simple word match comparison would select ads to be presented to these members.
This approach is problematic, however, because interests are self-reported by members. Many members who have a genuine interest in Chopin might not have explicitly listed Chopin as an interest in their profiles on a social network. As a result, an advertiser may miss out on members who have “incomplete” profiles—incomplete only in the sense that the profiles lack the information that the ad's targeting criteria is testing. Thus, the advertiser's reach is significantly reduced.
To counter this problem, a social network enables advertisers to extend the reach of their advertisements by leveraging information in the social network about a member with an incomplete profile. An advertisement may have targeting criteria that, for example, tests whether a member has listed “Britney” as an interest. Targeting criteria can be defined as a test or series of tests that can apply to a particular field in a member's profile. Traditionally, the interest field of a profile must list “Britney” in order for the ad to be presented to the member. However, embodiments of this invention enable advertisers to reach a broader base of members who may not have actually listed a targeted interest of the ad. This advertising technique infers a targeted interest for a member based on the interests listed on profiles of the member's connections.
“Inferential” ad targeting on a social network allows advertisers to reach members whose profiles fail to satisfy an ad's targeting criteria. For example, many members on a social network may not have listed Britney as an interest on their member profiles despite an actual interest in Britney. Advertisers may extend the reach of their advertisements to these members if the members' friends, or connections, actually list an interest in Britney on their profiles. Giving credence to the old adage “guilt by association,” the social network may, in one embodiment, infer an interest in Britney even though the member has not explicitly listed that particular interest in his or her profile. An “inferential ad” thus refers to an ad that allows targeting criteria to be satisfied by applying targeting criteria to the member's connections in the social network.
As illustrated in
The inferential ad targeting technique described above can be varied by advertisers according to the purposes of the advertising campaign. The targeting criteria of an inferential ad may be vary in complexity, may include secondary inferential targeting criteria to determine whether an ad should be included in a candidate set for a member, and also may include a threshold technique utilizing secondary inferential targeting criteria. The scope of inference can also be varied to include different numbers of connections, qualitatively distinct connections, and may include weighting connections by the member's affinity or another measure of closeness on the social network. Any combination of these techniques may be implemented by an advertiser to better refine the targeting criteria and scope of inference tailored to the needs of the advertising campaign.
An advertiser may implement targeting criteria for ads that vary in degrees of complexity. For example, an advertiser may simply target members that list certain keywords in their profiles, such as “canoeing.” More complex targeting may evaluate a function of a member's actions on the social network, such as, for example, identifying members who regularly click on videos posted by other members. The social network may identify behavioral characteristics of members on the social network and enable advertisers to target these characteristics.
Targeting criteria, in one embodiment, may also comprise “main” targeting criteria and “secondary” inferential targeting criteria. The main targeting criteria of an ad targets members of a social network and evaluates information on their profiles. Thus, the main targeting criteria of “canoeing” is satisfied if a member lists canoeing as an interest. Secondary inferential targeting criteria is used to determine if an ad should be presented to a member even though the member fails to satisfy the main targeting criteria. Secondary inferential targeting criteria is applied to the member's connections and may be the same as the main targeting criteria, or may differ to take into account the uncertainty of whether the member is actually interested in “canoeing,” as an example.
Secondary inferential targeting criteria may be as complex or as simple as desired. For example, suppose an advertiser implements complex targeting criteria that evaluates a member's proclivity to click on videos posted by a small subset of connections because the ad features a video. If the “main” targeting criteria establish a certain threshold for the measure of a member's proclivity to click on videos, a member may not meet that threshold. Additionally, a member may be new to the social network and, therefore, would not have the particular information being targeted. Secondary inferential targeting criteria may evaluate whether a certain threshold percentage of the member's connections meet the “main” criteria, or it may evaluate different criteria altogether, such as determining whether the member's connections have posted videos. The advertiser has tremendous flexibility in establishing targeting criteria in this respect.
Inferential ads may also differ in the scope of inference by varying the quantity and quality of connections included in the ad targeting process. For example, secondary inferential targeting criteria may include all of the member's connections in an attempt to infer an interest for the member, or an ad may focus on a smaller subset of the member's connections. The smaller subset of member's connections may be selected because of the member's affinity for those members, or because the smaller subset share a characteristic that the advertiser wishes to target, such as being alumni of the same college.
The quality of connections also may be varied to include multiple tiers of connections. An inferential ad may include only the member's direct connections or may include indirect connections, or the direct connections of the member's connections. For example, an advertiser may wish to target all alumni of specific colleges, in addition to other targeting criteria. A member who satisfies all of the other targeting criteria, but fails to list himself as an alum of one of the targeted colleges, would fail to satisfy the “main” targeting criteria. However, the targeting criteria may include secondary inferential targeting criteria to only evaluate the number of connections that have listed themselves as alums of the targeted colleges. The quality of connections can also be specified by the advertiser, meaning that indirect connections may also be included in the evaluation of the secondary inferential targeting criteria. Thus, if the secondary inferential targeting criteria, as defined by the advertiser, is satisfied, the member would be presented with the ad.
As already mentioned above, inferential ads may also include the ability to set thresholds for targeting criteria as applied to a member's connections. For example, an advertiser may determine that an ad may infer an interest for a member if more than 25% of the member's connections satisfy the secondary inferential targeting criteria or if at least 3 connections meet the main targeting criteria, or a combination of both. The ability to set thresholds for different types of targeting criteria contributes to the flexibility and refinement capabilities of embodiments of the invention.
The ad targeting algorithm may also weight the member's connections or otherwise take into account the member's affinity or other measure of closeness to the member's connections. In one embodiment, an expected click-through rate (ECTR) may be computed based on the affinity between the member and the connection. Measuring the affinity between members of a social network is well-known to those having ordinary skill in the art. An affinity score may also be called a coefficient of correlation because an affinity score indicates the strength of correlation between the member and a connection in the social network. Based on the interactions between the member and the connection, an affinity score is unidirectional, meaning that a member may have a high affinity for a connection but the same connection may have a low affinity for the member. Methods for determining affinities between members of a social network are described further in U.S. application Ser. No. 11/503,093, filed Aug. 11, 2006, entitled “Displaying Content Based on Measured User Affinity in a Social Network Environment,” hereby incorporated by reference in its entirety.
Any combination of the above targeting methods and ways of determining the scope of inference may be implemented in the ad targeting algorithm. In one embodiment, the advertiser has the ability to enable or disable the above features.
A web server 245 receives a request for a web page from a member device 265 as a member accesses the social network 200. The web server 245 requests an ad for the member from the ad server 225, specifically the ad targeting algorithm 205.
As shown in
The ad targeting algorithm 205 narrows the ad requests into a candidate set of inferred ads 230 using the information from the connections' profiles 260. The candidate ads 230 have targeting criteria 105 that matches the interests listed in the connections' profiles 260. An inferred ad selection algorithm 235 chooses one of the candidate ads 230 for presentation to the member whose profile does not list the information 250 being targeted. The selected inferred ad 240 is then sent to the web server 245 for presentation to the member device 265. In this way, an advertiser has extended the reach of an advertisement to a member who may not have been targeted because the social network lacked the information being evaluated for the member. In effect, the social network “fills the gap” by making an inference based on the profiles of the member's connections.
In
After this determination 330, the ad server 225 requests the member's connections' profiles 335 from the member profile store 215. The member profile store 215 returns the connections' profiles 340. Using the interests listed by the connections' profiles, the ad server 225 identifies a candidate set of ads and applies an algorithm to select an inferred ad for the member 345. The selected inferred ad is provided 360 to the web server 245. Finally, the web server 245 sends a web page comprising the selected inferred ad 365 to the member device 265.
At this point, each ad within the candidate set of ads is an inferred ad, meaning that an inference has been made to infer an interest for a member that did not explicitly list the inferred interest in the member's profile. However, there are multiple methods of selecting an inferred ad for a member. Each method serves different purposes suitable for various types of advertisers, large and small. By leveraging information in the social network, inferential ad targeting enables advertisers to select the most appropriate inferred ad for the advertising campaign.
In
Any number of variations and modifications can be made to the methods described above in selecting an ad for a member that are not illustrated herein. The social network is able to accommodate different types of advertising campaign objectives, including maximizing revenue and maximizing the user experience. Complex algorithms and customizations can be implemented to the above methods to achieve these objectives.
Learning Affinities Based on User Feedback from Inferential Ads
As described above, affinities between a member and the member's connections play an integral role in inferential ad targeting and selection. Improving and identifying erroneous affinities helps the social network provide better information to advertisers targeting audiences based on their interests, inferred or otherwise. In addition, the user experience is increased by identifying erroneous affinities because ads for items that actually interest the member are provided. Based on user feedback, affinities may be adjusted and incorporated into subsequent inferred ads. Likewise, if a member clicks on an inferred ad, that inferred ad may be queued for presentation to the member's connections as a result.
Using the member's feedback, affinity scores are recalculated 515 for the connections relied upon to select the inferred ad. Affinity scores would increase or decrease based on the feedback provided by the member. When a subsequent request for an inferred ad is received 520 for the member, the recalculated affinity scores will be used in selecting 525 an inferred ad for the member. The selection of the ad may comprise of any of the methods mentioned above, but would incorporated the recalculated, or “learned,” affinity scores of connections previously relied upon for inferential ad targeting.
Thus far, inferential ad targeting for a member has been described in terms of a lack of information listed on the member's profile, focusing on simple targeting criteria such as evaluations of fields in the member's profile and in the profiles of the member's connections. However, inferential ad targeting includes more complex targeting criteria based on member profile objects. Targeting criteria may include a test for anything that is targetable on a member profile object. A member profile object on a social network comprises basic demographic data and interests listed by the member, but also includes types of objects which the member interacts with frequently, such as polls, events, groups, pages, applications, links, notes, advertisements, photos, videos, status updates, as well as network information based on geographic location, school and college alumni status, and current and former employers.
For example, if a photo sharing service would like to advertise to members who tend to create and share photo albums, an advertisement could be targeted for member profiles exhibiting that behavioral characteristic. However, if a member has not created or shared photo albums, the advertiser may want to reach that member even though the member's profile object does not exhibit the targeted behavioral characteristic. Applying the inferential ad targeting technique described above, the member's connections' profile objects would be retrieved to infer the targeted characteristic. As a result, a targetable behavioral characteristic of a member's profile can be defined as anything existing on a member's profile upon which a test can be applied. If a test cannot be applied to a member for lack of information, the test can be applied to the member's connections to infer the missing information, in this case a behavioral characteristic, for the member.
Additionally, a member profile object may include information about the types of advertisers and advertisements that have been successful in advertising to the member. For example, if a member clicks on advertisements related to new cars, the behavioral data would be targetable via the member's profile object. If a member lacks that behavior characteristic, the member's connections' profile objects can be retrieved to infer the behavioral characteristic in the method described above. Also, metadata about the various types of advertisements on the social network, including social ads, interactive ads, banner ads, and fan pages, which have been successful in engaging member, are targetable via the member's profile object. For example, suppose a member has enjoyed watching video commercials and then commenting on the commercials within the social network. That behavior characteristic can be targeted by advertisers and can also be inferred using the inferential ad targeting technique described above. Countless behavioral characteristics may be targeted via member profile objects, and in turn, can also be inferred by the behavioral characteristics exhibited by the member's connections in a social network. Thus, behavioral characteristics exhibited by members are also targetable interests on member profiles.
Furthermore, inferential ad targeting may be implemented regardless of whether information is lacking in a member's profile. For example, if a member has an interest in surfing and has listed that interest on his profile, an ad with simple targeting criteria, such as a word matching algorithm, would be satisfied. However, more refined ad targeting criteria may be implemented using inferential ad targeting. Suppose that an advertiser wants to market surfboard products to a more serious surfer. Using the inferential ad targeting techniques described above, an advertiser would have more options to create more sophisticated targeting criteria. Such an advertiser may require that the member list the interest in surfing and be connected to 5 other members who also list an interest in surfing for the targeting criteria to be satisfied. Thus, the advertiser is able to target members with a more “extreme” interest using inferential ad targeting techniques.
Inferential ad targeting may be implemented in any context in which advertising is targeted to users based on their interests and the interests of other users connected to the user. Interests of a user may include behavioral characteristics described above. By applying the inferential ad targeting techniques described above on various platforms of information delivery, such as ad-hoc networks, peer-to-peer networks, mobile-to-mobile communications, and other such contexts, advertisers may extend the reach of their advertisements while delivering interesting and informative ads to users based on their interests, inferred or otherwise.
The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure.
Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof.
Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a tangible computer readable storage medium or any type of media suitable for storing electronic instructions, and coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
Embodiments of the invention may also relate to a computer data signal embodied in a carrier wave, where the computer data signal includes any embodiment of a computer program product or other data combination described herein. The computer data signal is a product that is presented in a tangible medium or carrier wave and modulated or otherwise encoded in the carrier wave, which is tangible, and transmitted according to any suitable transmission method.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based hereon. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
