[KDD 2020] SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter - CrossMinds.ai
[KDD 2020] SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter
Aug 13, 2020197 views
Jimmy Lin
Abstract: 
Personalized recommendation products at Twitter target a multitude of heterogeneous items: Tweets, Events, Topics, Hashtags, and,users. Each of these targets varies in their cardinality (which affects,the scale of the problem) and their “shelf life” (which constrains,the latency of generating the recommendations). Although Twitter,has built a variety of recommendation systems before dating back,a decade, solutions to the broader problem were mostly tackled,piecemeal. In this paper, we present SimClusters, a general-purpose,representation layer based on overlapping communities into which,users as well as heterogeneous content can be captured as sparse, interpretable vectors to support a multitude of recommendation tasks.,We propose a novel algorithm for community discovery based on,Metropolis-Hastings sampling, which is both more accurate and significantly faster than off-the-shelf alternatives. SimClusters scales,to networks with billions of users and has been effective across a,variety of deployed applications at Twitter.

Authors: Venu Satuluri: Twitter; Yao Wu: Twitter; Xun Zheng: Carnegie Mellon University; Yilei Qian: Twitter; Brian Wichers: Twitter; Qieyun Dai: Twitter; Gui Ming Tang: Twitter; Jerry Jiang: Twitter; Jimmy Lin: University of Waterloo

Paper URL: https://dl.acm.org/doi/10.1145/3394486.3403370
SIGKDD_2020
Applied Research
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