Abstract:
A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website presents targeted ads based on actions by the member and one or more characteristics of the member. The social networking website maintains a profile associated with the member which describes characteristics of the member, such as age, geographic location, employment, educational history and interests. The social networking website compares the member profile to targeting criteria for a plurality of advertising requests and determines the advertising requests that match the member profile and generate the most revenue for the social networking website. When presenting a member with an ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the ad.
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 mechanism is provided for searching a virtual resource in a large scale computing system environment. The virtual resource is deployed on at least one server. Each server is coupled to a sensor and communicates with the sensor. The sensors communicate with each other and consist of a communication network. Each sensor stores an identifier of a virtual resource deployed in a server connected with the sensor and the location information of the sensor itself. The mechanism receives a searching request for a virtual resource by the at least one sensor, the searching request containing an identifier of the virtual resource being searched; forwards the searching request in the communication network of the sensors; and returns a location information of a sensor storing the identifier of the virtual resource by the sensor itself.
Abstract:
Methods, apparatuses and systems directed to sponsored story generation from an organic activity stream in a social networking site. A user wishing to promote an entry from an organic activity stream may, using a sponsor user interface, specify the types of stories to promote to a portion of the home page displayed to a member of a social network.
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:
As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.
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 mechanism is provided for searching a virtual resource in a large scale computing system environment. The virtual resource is deployed on at least one server. Each server is coupled to a sensor and communicates with the sensor. The sensors communicate with each other and consist of a communication network. Each sensor stores an identifier of a virtual resource deployed in a server connected with the sensor and the location information of the sensor itself. The mechanism receives a searching request for a virtual resource by the at least one sensor, the searching request containing an identifier of the virtual resource being searched; forwards the searching request in the communication network of the sensors; and returns a location information of a senor storing the identifier of the virtual resource by the sensor itself.