User-item matching for recommendation fairness

TL;DR

This paper introduces a novel user-item matching approach that enhances recommendation fairness for item-providers by limiting item exposure based on past interactions, while maintaining or improving recommendation accuracy.

Contribution

It proposes a stock volume constraint method and a parameter-free MCMF-based model to improve item exposure fairness without sacrificing accuracy.

Findings

Achieves superior item exposure fairness compared to common recommenders.

Maintains or improves recommendation accuracy relative to baseline algorithms.

Parameter-free strategy simplifies implementation and tuning.

Abstract

As we all know, users and item-providers are two main parties of participants in recommender systems. However, most existing research efforts on recommendation were focused on better serving users and overlooked the purpose of item-providers. This paper is devoted to improve the item exposure fairness for item-providers' objective, and keep the recommendation accuracy not decreased or even improved for users' objective. We propose to set stock volume constraints on items, to be specific, limit the maximally allowable recommended times of an item to be proportional to the frequency of its being interacted in the past, which is validated to achieve superior item exposure fairness to common recommenders and thus mitigates the Matthew Effect on item popularity. With the two constraints of pre-existing recommendation length of users and our stock volumes of items, a heuristic strategy based on normalized scores and a Minimum Cost Maximum Flow (MCMF) based model are proposed to solve the optimal user-item matching problem, whose accuracy performances are even better than that of baseline algorithm in regular recommendation context, and in line with state-of-the-art enhancement of the baseline. What's more, our MCMF based strategy is parameter-free, while those counterpart algorithms have to resort to parameter traversal process to achieve their best performance.