Abstract:
An online system generates one or more models that determine a likelihood of a user interacting with an application over a particular time interval after installing the application. To generate the one or more models, the online system obtains information describing a user's interaction with the application that occurred greater than a threshold time period prior to a time for which user interaction with the application is to be determined. Example user interactions with the application include: usage of the application, numbers of particular interactions with the application, an amount of compensation the application receives from the user, interactions with other users of the application via the application, and any other suitable interactions. Various engagement metrics may be predicted by the one or more models such as an amount of time spent using the application, particular actions taken in the application, and revenue generated by the user in the application.
Abstract:
An online system allows a user to specify a sequence of advertisement requests (“ad requests”) where a set of rules identifies an order in which advertisements from the ds requests are presented to a user based on interactions by the user with presented advertisements from ad requests in the sequence. When a user interacts with an advertisement from an ad request from the sequence, the online system identifies an additional ad request from the sequence identified by a rule identifying the interaction by the user with the advertisement. The online system includes the additional ad request in one or more selection processes selecting content for presentation to the user. In some embodiments, the online system identifies an ad request from the sequence to include in the one or more selection processes based on likelihoods of the user interacting with advertisements from various ad requests in the sequence.
Abstract:
An online system applies advertising policies regulating presentation of sponsored content to its users. For example, advertising policies may prevent the presentation of advertisements in certain positions content feeds. The online system may relax an advertising policy for an advertisement meeting certain criteria, such as a likelihood of a user interacting with the advertisement or a predicted value of presenting the advertisement. If the online system relaxes an advertising policy for an advertisement, the online system computes a penalty incurred by the advertisement for violating the advertising policy. The online system computes a value for presenting a candidate feed presenting the advertisement in a position violating the advertising policy and a value for an alternative feed presenting the advertisement in a position complying with the advertising policy. The online system selects the candidate feed or the alternative feed for presentation to the user by comparing the values.
Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.
Abstract:
An online system accesses a plurality of posts associated with a quality metric used to subsidize or penalize an associated bid amount when competing for presentation. Each of the plurality of posts receives a quality rating from a professional rating service. The professional quality rating is considered to be ground truth. A mathematical function is used to describe the relationship between the professional quality rating and the quality metric determined by the system. The plurality of posts is segmented into categories. Based on whether the categories of posts fall above or below the mathematical function by more than a threshold amount, the system identifies an unfair subsidy or penalty associated with the category of content and adjusts the associated quality metric accordingly.
Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a minimum bid amount associated with advertisements eligible for presentation to the user. Increasing the minimum bid amount decreases the number of advertisements presented to the user while decreasing the minimum bid amount increases the number of advertisements presented to the user. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined. A target score is determined based on the engagement scores, and a difference between the target score and a threshold value is used to modify a minimum price of advertisements eligible for presentation to the user.
Abstract:
An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system generates the feed of content including organic content items and sends the feed of content to a client device while selecting sponsored content items for the feed of content. The online system transmits selected sponsored content items to the client device, which modifies the feed of content to include the sponsored content items and presents the modified feed of content.
Abstract:
An online system generates one or more models that determine a likelihood of a user interacting with an application over a particular time interval after installing the application. To generate the one or more models, the online system obtains information describing a user's interaction with the application that occurred greater than a threshold time period prior to a time for which user interaction with the application is to be determined. Example user interactions with the application include: usage of the application, numbers of particular interactions with the application, an amount of compensation the application receives from the user, interactions with other users of the application via the application, and any other suitable interactions. Various engagement metrics may be predicted by the one or more models such as an amount of time spent using the application, particular actions taken in the application, and revenue generated by the user in the application.
Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.
Abstract:
For ad campaigns including multiple advertisement (“ad”) requests each including an ad creative, which are automatically selected, a social networking system, or any other suitable online system, may bias selection of ad requests from an ad campaign towards early-selected ad requests with positive user interactions, limiting the number of ad requests selected from the ad campaign. To increase the likelihood of various advertisements in an ad campaign being evaluated for presentation to users, the social networking system modifies bid amounts associated with advertisements in the ad campaign using advertisement-specific bid adjustments based on interactions with the ad requests. Based on the modified bid amounts, the social networking system selects ad requests from the ad campaign to evaluate for presentation to a user.