Scaling Matrix Factorization for Recommendation with Randomness

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Provided by: Association for Computing Machinery
Topic: Data Management
Format: PDF
Recommendation is one of the core problems in eCommerce. In the authors' 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, they 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.
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