Abstract: 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.

Abstract: 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.

Abstract: A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

Abstract: A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

Abstract: A user's connections in a social networking system are grouped into a number of clusters based on a measure of the connections' relationships, or affinity, to each other. The affinities among the connections are based on the connections' own relationships and indicate a likelihood that the connections are in the same social circles. The clusters are formed based on the affinities among the user's connections, where the clusters tend to have connections that have relatively high affinities with the other connections the same cluster as compared to the connections who are not in the same cluster. An iterative hierarchical clustering algorithm may be used to collapse the connections into clusters based on affinities between pairs of the connections.

Abstract: A social networking system selects a subset of keywords from a set of master keywords found in user profiles. The method includes selecting a first and second group of user profiles including one or more keywords and computing the number of occurrences of each of the master keywords in the first and second group of profiles. A value may be computed for each of the master keywords based on a comparison of the number of occurrences in the first group of profiles and the number of occurrences in the second group of profiles. The computed value may be used for selecting the subset of keywords from the master keywords and/or ranking the master keywords.

Abstract: A social networking system selects a subset of keywords from a set of master keywords found in user profiles. The method includes selecting a first and second group of user profiles including one or more keywords and computing the number of occurrences of each of the master keywords in the first and second group of profiles. A value may be computed for each of the master keywords based on a comparison of the number of occurrences in the first group of profiles and the number of occurrences in the second group of profiles. The computed value may be used for selecting the subset of keywords from the master keywords and/or ranking the master keywords.

Abstract: A social networking system selects a subset of keywords from a set of master keywords found in user profiles. The method includes selecting a first and second group of user profiles including one or more keywords and computing the number of occurrences of each of the master keywords in the first and second group of profiles. A value may be computed for each of the master keywords based on a comparison of the number of occurrences in the first group of profiles and the number of occurrences in the second group of profiles. The computed value may be used for selecting the subset of keywords from the master keywords and/or ranking the master keywords.

Abstract: A social networking system generates socially-relevant ads for a user based on actions taken by other users to whom the user is connected. These may be actions taken on, and determined by, an external system, information about which actions can be received by the social networking system and used for matching to an advertisement request. The social networking system may determine a social context for advertising to a user based on the action and may generate an advertisement for the matched ad request that includes an informational message communicating the social context (i.e., an ad with the information message appended to it, a news story for the informational message, etc.). The ad may be targeted to the user based on user affinity for the ad, based on an object interacted with or an action taken by the user's connections on or external to the social networking system, among other criteria.

Abstract: A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

Abstract: A social networking system generates socially-relevant stories for a user based on other users (e.g., based on actions taken by other users) to whom the user is connected. These socially-relevant stories, including news stories, social advertisements, etc., may be presented on a web page within a domain of an external system that is different from the domain of the social networking system. When a web page from an external system is requested for a viewing user, the social content about other users who are connected to the viewing user may be provided by the social networking system for that viewing user. This personalized social content may be presented in a frame (e.g., an iframe) of the external web page rendered and provided for display to the viewing user.

Abstract: A social networking system generates socially-relevant stories for a user based on other users (e.g., based on actions taken by other users) to whom the user is connected. These socially-relevant stories, including news stories, social advertisements, etc., may be presented on a web page within a domain of an external system that is different from the domain of the social networking system. When a web page from an external system is requested for a viewing user, the social content about other users who are connected to the viewing user may be provided by the social networking system for that viewing user. This personalized social content may be presented in a frame (e.g., an iframe) of the external web page rendered and provided for display to the viewing user.

Abstract: In accordance with aspects disclosed herein, impact that a user interface design has on a user's experience is measured. User response to one or more user interface designs is measured. Two or more user interface designs can be compared based on user response to each design. A single user interface design can be evaluated based on one or more measures of user response to the user interface, or two or more user interface designs can be compared using one or more user response measures corresponding to the user interface designs.

Abstract: A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

Abstract: A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website generates socially relevant ads for the member based on the actions logged for other members on the website to whom the member is connected (i.e., the member's online friends). The advertiser associated with the social ad may compensate the social networking website for publishing the ad on the website. When presenting a member with a social ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the social ad. The expected value may be computed according to a function that includes the member's affinity for the ad content and the bid amount. The technique is also applied for providing socially relevant information off the social networking website.

Abstract: A user's connections in a social networking system are grouped into a number of clusters based on a measure of the connections' relationships, or affinity, to each other. The affinities among the connections are based on the connections' own relationships and indicate a likelihood that the connections are in the same social circles. The clusters are formed based on the affinities among the user's connections, where the clusters tend to have connections that have relatively high affinities with the other connections the same cluster as compared to the connections who are not in the same cluster. An iterative hierarchical clustering algorithm may be used to collapse the connections into clusters based on affinities between pairs of the connections.

Abstract: A system and method for selecting users of a web-based social network who are likely to respond to an invitation, each of the users having associated profile information is disclosed. The method includes selecting pilot users and a reduced set of keywords from the profile information. The method further includes sending the invitation to the pilot users, receiving responses from the pilot users, and classifying the responses as either positive or negative. A training set of vector pairs is created each vector pair representing a pilot user and including data representing a classified response and training keywords selected from the reduced set of keywords and associated profile information for the pilot user. A function is determined based on the vectors and used to calculate a likelihood that each of one or more users of the web based social network will respond to the invitation.

Abstract: A social networking system predicts a user's top friends among the user's connections in a social networking system. A top friend prediction model receives static data and statistics related to the historical interactions of the connection and the user as input singles. The model may be trained using a training set of data associated with the connections of users, where users have explicitly indicated that other users are or are not their top (or “best” or “closest”) friends. The trained model outputs a score for each of a particular user's connections, and the score is used to predict whether the connection is a top friend of that user. Whether a user's connection is one of that user's top friends thus indicates a closeness of that relationship in the real world, which may differ from how likely the users are to interact with each other within the social networking system.

Abstract: 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.

Abstract: A social networking system generates socially-relevant stories for a user based on other users (e.g., based on actions taken by other users) to whom the user is connected. These socially-relevant stories, including news stories, social advertisements, etc., may be presented on a web page within a domain of an external system that is different from the domain of the social networking system. When a web page from an external system is requested for a viewing user, the social content about other users who are connected to the viewing user may be provided by the social networking system for that viewing user. This personalized social content may be presented in a frame (e.g., an iframe) of the external web page rendered and provided for display to the viewing user.