WWW2007 Paper Details
Track:
Industrial Practice and Experience
Paper Title:
Google News Personalization: Scalable Online Collaborative Filtering
Authors:
  • Abhinandan Das (Google)
  • Mayur Datar (Google)
  • Ashutosh Garg (Google)
  • Shyam Rajaram (University of Illinois at Urbana Champaign)
Abstract:
Several approaches to collaborative filtering have been studied but seldom have the studies been reported for large (several millions of users 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 adaptible 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.
Slot:
New Brunswick, Thursday, May 10, 2007, 1:30pm to 3:00pm.
Full-text:
PDF version