Collaborative Filtering Based on Factorization and Distance Metric Learning
Yuanle Deng, Ming Ye, Long Xiong · 2019
Collaborative filtering predicts user preferences by modeling the user's historical behavior. Matrix factorization plays an important role in collaborative filtering. Matrix factorization use dot product to predicts rating. Nevertheless, the dot product does not satisfy the triangle inequality, which may limit their expressiveness and lead to sub-optimal solutions. This paper combines metric learning and distance factorization. Firstly, convert the scoring matrix to a distance matrix. Then the distance matrix is factorization by metric learning to obtain the position of users and items in low-dimensional Euclidean space. User preferences and distance are negatively correlated, the user is closer to the favorite item and farther away from the unloved item, that is, the user's preference is reflected by the distance. Experiments conducted on real-world data sets have shown that compared with classical algorithm, our method significantly improves the accuracy of recommendations.