document: Google news personalization : scalable online collaborative filtering 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 .
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