Track: Industrial Practice and Experience
Paper Title:
Google News Personalization: Scalable Online Collaborative Filtering
Authors:
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.
