This paper introduce an approach to collaborative filtering that can give Google News users personalized recommendation.

Das, Abhinandan & Datar, Mayur & Garg, Ashutosh & Rajaram, ShyamSundar. (2007). Google news personalization: Scalable online collaborative filtering. 16th International World Wide Web Conference, WWW2007. 271-280. 10.1145/1242572.1242610. Retrieve from https://www.researchgate.net/publication/221023652_Google_news_personalization_Scalable_online_collaborative_filtering

Several approaches to collaborative filtering have been stud- ied 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 de- scribe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collabo- rative filtering using MinHash clustering, Probabilistic La- tent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and con- sequently 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.