Scaling matrix factorization for recommendation with randomness
Lei Tang, Patrick Harrington · 2013
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.