个性化LinkedIn Feed

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2015-08-10 DOI:10.1145/2783258.2788614

D. Agarwal, Bee-Chung Chen, Qi He, Zhenhao Hua, Guy Lebanon, Yiming Ma, Pannagadatta K. Shivaswamy, Hsiao-Ping Tseng, Jaewon Yang, L. Zhang

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引用次数: 39

摘要

LinkedIn动态地将用户人际网络中的更新活动发送给超过3亿的个性化信息源，并根据与用户的“相关性”对活动进行排名。本文揭示了LinkedIn个性化feed系统背后的实现细节，这是相关工作中无法找到的，并解决了在线部署系统的可扩展性和数据稀疏性挑战。更具体地说，我们通过生成三种亲和度分数来关注个性化模型:查看者-活动类型亲和度、查看者-参与者亲和度和查看者-参与者-活动类型亲和度。基于在线桶测试(A/B实验)和离线评估的大量实验说明了我们的个性化模型在LinkedIn feed中的效果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Personalizing LinkedIn Feed

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.

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Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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