Abstract: A method and system for determining links for a linking page are provided. For each linking page of a plurality of linking pages, a similarity score between each linking page and each linked-to page is generated. Each linked-to page is associated with each linking page. Based on the similarity scores generated for each linking page, a set of linked-to pages for each linking page is identified. An importance score for the linking page is also generated. Identities of the plurality of linking pages, identities of the set of linked-to pages identified for each linking page, and the importance score for each linking page are stored as input. Based on the input, for each linking page, a subset of the set of linked-to pages identified for each linking page is assigned by a model.

Abstract: Techniques for automatically linking pages in a web site are provided. In one technique, training data for a machine-learned scoring model is generated that comprises a plurality of features related to content items. The training data comprises multiple entries, each corresponding to a different content item in a first set of content items. For each entry, a corresponding label is based on a ranking of the corresponding content item in one or more search engine results. The machine-learned scoring model is trained based on the training data. For each content item in a second set of content items, multiple attribute values associated with that content item are input into the machine-learned scoring model, which generates a result. Based on multiple results, determining, for a particular web page, a strict subset of the second set of content items to which the particular web page will include one or more links.

Abstract: Techniques for discovering related organizations through different types of online connections are provided. In one technique, connection data is stored that identifies, for each user in a first set of users, one or more other users with which that user has a connection. Job change data is stored that identifies, for each user of a second set of users, multiple organizations for which that user has worked or had sought an employment relationship. Based on the connection data, a number of connections between employees of a first organization and employees of a second organization is identified. Based on the job change data, a number of users that listed, in their respective online profiles, the first organization as an employer is identified. Based on the number of connections and the number of users, a determination of whether the first organization and the second organization are related is made.

Abstract: Techniques for automatically linking pages in a web site are provided. In one technique, training data for a machine-learned scoring model is generated that comprises a plurality of features related to content items. The training data comprises multiple entries, each corresponding to a different content item in a first set of content items. For each entry, a corresponding label is based on a ranking of the corresponding content item in one or more search engine results. The machine-learned scoring model is trained based on the training data. For each content item in a second set of content items, multiple attribute values associated with that content item are input into the machine-learned scoring model, which generates a result. Based on multiple results, determining, for a particular web page, a strict subset of the second set of content items to which the particular web page will include one or more links.

Abstract: Techniques are provided for determining a propensity or likelihood that a person in a social network will perform a particular action. A statistical model that is trained based on multiple features of each member in a first plurality of members of a social network is stored. The multiple features include a subset pertaining to profile information provided by the first plurality of members and stored in a plurality of profiles of the first plurality of members. For each member of a second plurality of members of the social network, multiple feature values that correspond to the plurality of features are identified, the statistical model is used to generate a score for the member. The score indicates a likelihood that the member will perform the particular action.

Abstract: Techniques are provided for assigning members of a group to multiple target entities. A social proximity score is generated that reflects a measure of social relatedness between a group member and a target entity. Generated social proximity scores and one or more constraints are specified or referenced by a program. The program seeks to maximize social proximity scores in light of the one or more constraints. A solver executes the program to generate a result that assigns each target entity to at least one (or no more than one) group member.