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arXiv · 1909.04239

PMD: An Optimal Transportation-based User Distance for Recommender Systems

Yitong Meng·Xinyan Dai·Xiao Yan·James Cheng·Weiwen Liu·Benben Liao·Jun Guo·Guangyong Chen
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Abstract

Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. As a result, these measures cannot fully utilize the rating information and are not suitable for real world sparse data. To solve these issues, we propose a novel user distance measure named Preference Mover's Distance (PMD) which makes full use of all ratings made by each user. Our proposed PMD can properly measure the distance between a pair of users even if they have no co-rated items. We show that this measure can be cast as an instance of the Earth Mover's Distance, a well-studied transportation problem for which several highly efficient solvers have been developed. Experimental results show that PMD can help achieve superior recommendation accuracy than state-of-the-art methods, especially when training data is very sparse.

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Yitong Meng, Xinyan Dai, Xiao Yan, James Cheng, Weiwen Liu, Benben Liao, Jun Guo, Guangyong Chen. 2019-09-10. PMD: An Optimal Transportation-based User Distance for Recommender Systems. https://arxiv.org/abs/1909.04239

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