Wasserstein Collaborative Filtering for Item Cold-start Recommendation
Yitong Meng, Xiao Yan, Weiwen Liu, Huanhuan Wu, James Cheng
Abstract
Item cold-start recommendation, which predicts user preference on new items that have no user interaction records, is an important problem in recommender systems. In this paper, we model the disparity between user preferences on warm items (those having interaction record) and that on cold-start items using the Wasserstein distance. On this basis, we propose Wasserstein Collaborative Filtering (WCF), which predicts user preference on cold-start items by minimizing the Wasserstein distance under user embedding constraint. Our analysis shows that minimizing the Wasserstein distance ensures that users sharing similar tastes on warm items also have similar preferences on cold-start items. Experimental results show that WCF consistently outperform the state-of-the-art methods in recommendation quality, usually by a large margin.