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

Abstract

Collaborative filtering, a widely-used recommendation technique, predicts a user's preference by aggregating the ratings from similar users. Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of 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 wellstudied transportation problem for which several highly efficient solvers have been developed. Experimental results show that PMD can help achieve superior recommendation accuracy than state-ofthe-art methods, especially when training data is very sparse. arXiv:1909.04239v1 [cs.IR]

A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search

“…Nearest Neighbor Search (NNS) is a fundamental building block in various application domains [7,8,35,64,67,76,101,110], such as information retrieval [31,111], pattern recognition [26,54], data mining [41,44], machine learning [21,25], and recommendation systems [66,78]. With the explosive growth of datasets' scale and the inevitable curse of dimensionality, accurate NNS cannot meet This work is licensed under the Creative Commons BY-NC-ND 4.0 International License.…”