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Google news personalization: scalable online collaborative filtering

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Google news personalization: scalable online collaborative filtering
Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several million 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 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. Categories and Subject Descriptors: H.4.m [Information Systems]: Miscellaneous General Terms: Algori...
Abhinandan Das, Mayur Datar, Ashutosh Garg, ShyamS
Added 21 Nov 2009
Updated 21 Nov 2009
Type Conference
Year 2007
Where WWW
Authors Abhinandan Das, Mayur Datar, Ashutosh Garg, ShyamSundar Rajaram
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