Microsoft Technology Licensing, LLC

Andrew O Hatch

Yan Wang

Michael Conover

Ankit Gupta

Keqing Liang

Da Xu

Timothy Paul Jurka

Patrick A Chase

Claire D McGinty

Alejandro Jorge Perez

Samuel Stokes

Gregory W Bayer

Akshay D Kothari

Bixing Yan

Haohua Wan

Techniques for a social networking system involve analyzing members' interactions to enhance follow recommendations. The techniques include calculating the number of follows generated by a first member and the number of follows received by a second and third member. The platform then computes a weighted follow utility score for two member pairs: the first comprising the first and second members, and the second comprising the first and third members. These scores are determined by equally weighing the follows generated and received, along with a probability of the first member following the second or third member. While the probability suggests a preference for the second member, the ranking system places the third member pair higher. Consequently, the first member is advised to follow the third member, based on this ranking. Finally, this recommendation is displayed to the first member on an electronic device, guiding their social interactions on the platform.

Augmented follow probability for social networking system follow recommendations

The disclosed embodiments provide a system for processing data. During operation, the system obtains one or more metrics associated with user interaction with a set of content items in a member segment of a social network. Next, the system ranks the set of content items by the one or more metrics. The system then generates a content pool for the member segment based on the ranking of the content items. Finally, the system creates a content feed from the content pool for presentation to a user in the member segment.

Segment-based content pools for inclusion in content feeds

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Berkeley, CA, United States of America

Andrew O Hatch