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Google news personalization: scalable online collaborative filtering
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| Venue: | in WWW, 2007 |
| Citations: | 278 - 0 self |
BibTeX
@INPROCEEDINGS{Das_googlenews,
author = {Abhinandan Das and Mayur Datar and Shyam Rajaram and Ashutosh Garg},
title = {Google news personalization: scalable online collaborative filtering},
booktitle = {in WWW, 2007},
year = {},
pages = {271--280}
}
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Abstract
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.
Keyphrases
scalable online collaborative filtering google news personalization google news system setup minhash clustering collaborative filtering personalized recommendation probabilistic latent semantic indexing report result recommendation engine several approach different algorithm covisitation count linear model minimal effort
