2014 ActivityRankinginLinkedInFeed

• (Agarwal et al., 2014) ⇒ Deepak Agarwal, Bee-Chung Chen, Rupesh Gupta, Joshua Hartman, Qi He, Anand Iyer, Sumanth Kolar, Yiming Ma, Pannagadatta Shivaswamy, Ajit Singh, and Liang Zhang. (2014). “Activity Ranking in LinkedIn Feed.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623362

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Activity+Ranking+in+LinkedIn+Feed
• http://dl.acm.org/citation.cfm?id=2623330.2623362&preflayout=flat#citedby

Quotes

Author Keywords

• Activity ranking; large scale learning; miscellaneous; relevance

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ActivityRankinginLinkedInFeed	Bee-Chung Chen Deepak Agarwal Qi He Yiming Ma Pannagadatta Shivaswamy Rupesh Gupta Joshua Hartman Anand Iyer Sumanth Kolar Ajit Singh Liang Zhang			Activity Ranking in LinkedIn Feed				10.1145/2623330.2623362		2014