MatMat: Matrix Factorization by Matrix Fitting
This work addresses a specific bottleneck in recommender systems for users and developers by enabling better integration of side information and multi-task learning, though it is incremental as it builds on existing matrix factorization techniques.
The paper tackled the problem of matrix factorization's limitations in incorporating side information and multi-task learning by replacing scalar rating values with matrices, fitting them via matrix products of user and item feature matrices. The result demonstrated improved performance in accuracy and fairness metrics compared to other approaches.
Matrix factorization is a widely adopted recommender system technique that fits scalar rating values by dot products of user feature vectors and item feature vectors. However, the formulation of matrix factorization as a scalar fitting problem is not friendly to side information incorporation or multi-task learning. In this paper, we replace the scalar values of the user rating matrix by matrices, and fit the matrix values by matrix products of user feature matrix and item feature matrix. Our framework is friendly to multitask learning and side information incorporation. We use popularity data as side information in our paper in particular to enhance the performance of matrix factorization techniques. In the experiment section, we prove the competence of our method compared with other approaches using both accuracy and fairness metrics. Our framework is an ideal substitute for tensor factorization in context-aware recommendation and many other scenarios.