Mayur Datar on Content-Based and Collaborative Filtering

Mayur Datar on Content-Based and Collaborative Filtering
The Association for Computing Machinery - Association for Computing Machinery (ACM)
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.Research scientist Mayur Datar describes 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. Datar describes our algorithms and system setup in detail and reports results of running the recommendations engine on Google News - Association for Computing Machinery