Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation

TL;DR

This paper introduces a stable matching-based post-processing method to balance accuracy and diversity in recommendations, effectively increasing catalog coverage without significantly sacrificing user satisfaction.

Contribution

It presents a novel generalization of the Deferred Acceptance algorithm for recommendation systems, optimizing both user and item utilities to improve diversity and fairness.

Findings

Increases aggregate diversity and item utility significantly.

Maintains comparable recommendation accuracy to baseline methods.

Proves the stability and uniqueness of the user-optimal match.

Abstract

Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains where it may be desirable to mitigate the popularity bias and to improve the coverage of long-tail items in recommendations given to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user, but also for other stakeholders such as item sellers or producers who desire a fair representation of their items across recommendation lists produced by the system. Unfortunately, attempts to increase aggregate diversity often result in lower recommendation accuracy for end users. Thus, addressing this problem requires an approach that can effectively manage the trade-offs between accuracy and aggregate diversity. In this work, we propose a two-sided post-processing approach in which both user and item utilities are considered. Our goal is to maximize aggregate diversity while minimizing loss in recommendation accuracy. Our solution is a generalization of the Deferred Acceptance algorithm which was proposed as an efficient algorithm to solve the well-known stable matching problem. We prove that our algorithm results in a unique user-optimal stable match between items and users. Using three recommendation datasets, we empirically demonstrate the effectiveness of our approach in comparison to several baselines. In particular, our results show that the proposed solution is quite effective in increasing aggregate diversity and item-side utility while optimizing recommendation accuracy for end users.