FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems
This addresses the issue of bias toward popular items in recommender systems, which can limit item coverage and fairness for users, though it is an incremental improvement as it builds on existing graph-based methods.
The paper tackles the problem of low aggregate diversity and unfair distribution in recommender systems, where popular items dominate recommendations, by introducing FairMatch, a graph-based post-processing algorithm that improves aggregate diversity while maintaining comparable recommendation accuracy, as shown in experiments on two datasets.
Recommender systems are often biased toward popular items. In other words, few items are frequently recommended while the majority of items do not get proportionate attention. That leads to low coverage of items in recommendation lists across users (i.e. low aggregate diversity) and unfair distribution of recommended items. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation for improving aggregate diversity. The algorithm iteratively finds items that are rarely recommended yet are high-quality and add them to the users' final recommendation lists. This is done by solving the maximum flow problem on the recommendation bipartite graph. While we focus on aggregate diversity and fair distribution of recommended items, the algorithm can be adapted to other recommendation scenarios using different underlying definitions of fairness. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improving aggregate diversity, provides comparable recommendation accuracy.