An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.

Techniques are provided for building a unified model for selecting content items of different types in response to receiving electronic content requests transmitted over a network. In one technique, in response to a request, multiple content items are identified. The multiple content items include a first content item of a first type and a second content item of a second type. A first engagement value that indicates a first level of engagement of an online resource for content items of the first type is determined. A first predictive user selection rate is generated for the first content item based on the first engagement value. A second predictive user selection rate is generated for the second content item. The multiple content items are ranked based, at least in part, on the predictive user selection rates. A particular content item is then selected based on the predictive user selection rates.

A social networking system generates socially-relevant stories for a user based on actions taken by other users to whom the user is connected. The social networking system may receive a request for a sponsored story for a viewing user and may select information about one or more actions performed by one or more users to whom the viewing user is connected to identify one of a plurality of candidate information for a sponsored story based on one or more criteria (e.g., affinity of the viewing user for the candidates, expected value for the candidates, etc.). The social networking system may also generate the sponsored story and generate a feed comprising the sponsored story and news stories (e.g., non-sponsored stories) about one or more users of the social networking system with whom the viewing user is connected. This feed may be provided for display to the viewing user.

Disclosed are various embodiments for determining an expected number of times that content may be served in conjunction with a user interface. A forecasting model for determining an expected bid requests inventory is generated, wherein a bid request indicates an opportunity to submit a bid to present content in conjunction with a user interface. A bid success rate is determined. An expected impressions inventory is determined based on the expected bid requests inventory and the bid success rate.

A system and methods for simulating human usage of mobile devices by simulating human behavior patterns operating mobile devices and using the simulation of human usage to obtain advertising or other online content specific to certain entities that is displayed on the mobile devices. The advertising or other online content specific to the certain entities is transmitted to an analytics server, where the advertising or other online content is interpreted and deciphered to obtain particular elements that relate to the advertising and online content including, but not limited to, an identification of the specific entity, the size of the advertising or other online content, the locations where the advertising or other online content appears, and the path by which the advertising or other online content travels to the mobile devices for display.

Disclosed are a method and device for displaying APP recommendation information. The method may include: acquiring a CVR threshold for an APP, wherein a difference between an installation parameter corresponding to the CVR threshold and a desired installation parameter is within a preset range, the installation parameter is related to an installation number and generated from historical statistical information in which a CVR is equal to or higher than the CVR threshold, and the historical statistical information is acquired from historical displays of the APP recommendation information of the APP; predicting a CVR for the APP recommendation information to be displayed; and determining whether the CVR predicted is lower than the CVR threshold, if yes, not displaying the APP recommendation information, if no, displaying the APP recommendation information.

Social media networking applications, web sites, and services creates implicit relationships between users based on their interest or participation in real-world and optionally virtual or online activities in addition to explicitly defined peer relationships. User profiles, activity entities, and expressions may be associated with metadata to assist in searching and navigation. Metadata is implicitly associated with user profiles, activity entities, expressions, or other data entities based on user behavior using metadata collector. A metadata collector is a poll, survey, list, questionnaire, census, test, game, or other type of presentation adapted to solicit user interaction. A metadata collector is associated with metadata elements. When users interact with a metadata collector, their user profiles and the data entities included in their interactions become associated with the metadata elements of the metadata collector. These metadata element associations may then be used for any purpose.

Portable devices include environmental sensors that generate metrics about the environment (e.g., accelerometers detecting impulses and vibration, and GPS receivers detecting position and velocity). Such devices often use environmental metrics to extract user input directed at the device by the user, and status information about the device and the environment. Presented herein are techniques for using environmental metrics to infer physical activities performed by the user while attached to the device. For example, jogging may be inferred from regular, strong impulses and typical jogging speed; walking may be inferred from regular, weak impulses and typical walking speed; and riding in a vehicle may be inferred from low-level vibrations and high speed (optionally identifying the type of vehicle ridden by the user). Based on these inferences, the device may automatically present applications and/or or adjust user interfaces suitable for the user's physical activity, rather than responsive to user input.

Machine readable instructions for providing targeted advertisements for a piece of digital content receive a set of user metadata for a group of users. The machine readable instructions select at least one user from the group of users. In addition, the machine readable instructions select a term from a category of terms related to the piece of digital content. Moreover, the machine readable instructions determine a first value corresponding to the at least one user. Also, the machine readable instructions determine a second for the group of users. In addition, the machine readable instructions determine a user score based at least in part on the first value and the second value. When the user score is within a particular range, the machine readable instructions provide an advertisement for the piece of digital content to an electronic device associated with the at least one user.

In one aspect, a computing apparatus is configured to represent offer rules based on requirements for the detection of predefined types of events and actions scheduled to be performed in response to the detection of each occurrence of the events. The events are independent from each other in processing and are linked via prerequisite conditions to formulate the requirements of an offer campaign. The computing apparatus is configured to store data indicating the completion statuses of the events and process the events, including the scheduled actions, if any, in an atomic way. Thus, the offer rules can be changed on-the-fly during run time execution by the computing apparatus.