Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Large-Scale Parallel Collaborative Filtering for the Netflix Prize
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
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Recommendation is one of the core problems in eCommerce. In our application, different from conventional collaborative filtering, one user can engage in various types of activities in a sequence. Meanwhile, the number of users and items involved are quite huge, entailing scalable approaches. In this paper, we propose one simple approach to integrate multiple types of user actions for recommendation. A two-stage randomized matrix factorization is presented to handle large-scale collaborative filtering where alternating least squares or stochastic gradient descent is not viable. Empirical results show that the method is quite scalable, and is able to effectively capture correlations between different actions, thus making more relevant recommendations.