[ACM Press the 20th ACM SIGKDD international conference - New York, New York, USA (2014.08.24-2014.08.27)] Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14 - Activity ranking in LinkedIn feed

✍ Scribed by Agarwal, Deepak; Singh, Ajit; Zhang, Liang; Chen, Bee-Chung; Gupta, Rupesh; Hartman, Joshua; He, Qi; Iyer, Anand; Kolar, Sumanth; Ma, Yiming; Shivaswamy, Pannagadatta

• Book ID: 127081021
• Publisher: ACM Press
• Year: 2014
• Weight: 516 KB
• Category: Article
• ISBN: 145032956X
• DOI: 10.1145/2623330.2623362

✦ Synopsis

Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a user's network neighborhood forms a stream of updates for the user's consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.