Microsoft Technology Licensing, LLC

Yi Feng

Weizhen Wang

Dong Li

Mudit Goel

Xuan Li

Min Lu

Techniques for presenting a recommendation to a member of a social network in a specific field of study are described. A predictor can access, from a database in the social network, educational data of a plurality of students and post-graduate data of a plurality of graduates in the social network. Additionally, the predictor can determine a subset of students associated with a specific field of study, and can calculate a demand for the specific field of study based on the accessed data. A recommendation generator can calculate a competition value for the specific field of study based on the determined subset of students and the calculated demand for the specific field of study. Subsequently, the recommendation generator can cause a presentation, on a display of a device, of a recommendation associated with the specific field of study, the recommendation being based on the calculated competition value.

Calculating supply and demand for a field of study

Techniques for ranking members based on an influence score are described. For example, an influence score process can access, from a database in an online social network, member data of a first member in response to receiving a ranking request of the first member. Additionally, a contribution factor for each first-degree connection of the first member can be determined. A centrality score for the first member can be determined based on the contribution factor for each first-degree connection of the first member. Moreover, an expertise score for the first member can be determined based on the member data of the first member. Furthermore, an influence score of the first member can be calculated based on the centrality score and the expertise score. Subsequently, a user interface is configured to cause a presentation, on a display of a device, of the ranking of the first member based on the influence score.

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Sunnyvale, CA, United States of America

Yi Feng