PMD: An Optimal Transportation-based User Distance for Recommender Systems
This addresses the challenge of improving recommendation accuracy in real-world sparse data for users and platforms, representing an incremental advancement by adapting optimal transportation theory to a known bottleneck in collaborative filtering.
The paper tackles the problem of measuring user similarity in collaborative filtering for recommender systems, especially with sparse data, by proposing a novel user distance measure called Preference Mover's Distance (PMD) that utilizes all ratings and handles cases with no co-rated items, achieving superior recommendation accuracy compared to state-of-the-art methods, particularly in sparse training data scenarios.
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.