Google news personalization: scalable online collaborative filtering

  • Authors:
  • Abhinandan S. Das;Mayur Datar;Ashutosh Garg;Shyam Rajaram

  • Affiliations:
  • Google Inc., Mountain View, CA;Google Inc., Mountain View, CA;Google Inc., Mountain View, CA;University of Illinois at Urbana Champaign, Urbana Champaign, IL

  • Venue:
  • Proceedings of the 16th international conference on World Wide Web
  • Year:
  • 2007

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Abstract

Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several millionusers and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.