2015 PersonalizingLinkedInFeed

• (Agarwal et al., 2015) ⇒ Deepak Agarwal, Bee-Chung Chen, Qi He, Zhenhao Hua, Guy Lebanon, Yiming Ma, Pannagadatta Shivaswamy, Hsiao-Ping Tseng, Jaewon Yang, and Liang Zhang. (2015). “Personalizing LinkedIn Feed.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2788614

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Personalizing+LinkedIn+Feed
• http://dl.acm.org/citation.cfm?id=2783258.2788614&preflayout=flat#citedby

Quotes

Author Keywords

• Feed relevance; large scale learning; miscellaneous; personalization

Abstract

LinkedIn dynamically delivers update activities from a user's interpersonal network to more than 300 million members in the personalized feed that ranks activities according their “relevance” to the user. This paper discloses the implementation details behind this personalized feed system at LinkedIn which can not be found from related work, and addresses the scalability and data sparsity challenges for deploying the system online. More specifically, we focus on the personalization models by generating three kinds of affinity scores: Viewer-ActivityType Affinity, Viewer-Actor Affinity, and Viewer-Actor-ActivityType Affinity. Extensive experiments based on online bucket tests (A/B experiments) and offline evaluation illustrate the effect of our personalization models in LinkedIn feed.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 PersonalizingLinkedInFeed	Bee-Chung Chen Deepak Agarwal Qi He Yiming Ma Pannagadatta Shivaswamy Guy Lebanon Jaewon Yang Liang Zhang Zhenhao Hua Hsiao-Ping Tseng			Personalizing LinkedIn Feed				10.1145/2783258.2788614		2015