A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System

TL;DR

This paper introduces a counterfactual method to identify and reduce individual user unfairness in collaborative filtering recommender systems, improving fairness and recommendation quality for under-served users.

Contribution

It proposes a novel counterfactual approach that mitigates individual user unfairness by simulating new interactions, enhancing personalized recommendations.

Findings

Outperforms existing unfairness mitigation techniques

Improves recommendation fairness for under-served users

Validated on MovieLens and Amazon datasets

Abstract

Recommender Systems (RSs) are exploited by various business enterprises to suggest their products (items) to consumers (users). Collaborative filtering (CF) is a widely used variant of RSs which learns hidden patterns from user-item interactions for recommending items to users. Recommendations provided by the traditional CF models are often biased. Generally, such models learn and update embeddings for all the users, thereby overlooking the biases toward each under-served users individually. This leads to certain users receiving poorer recommendations than the rest. Such unfair treatment toward users incur loss to the business houses. There is limited research which addressed individual user unfairness problem (IUUP). Existing literature employed explicit exploration-based multi-armed bandits, individual user unfairness metric, and explanation score to address this issue. Although, these works elucidate and identify the underlying individual user unfairness, however, they do not provide solutions for it. In this paper, we propose a dual-step approach which identifies and mitigates IUUP in recommendations. In the proposed work, we counterfactually introduce new interactions to the candidate users (one at a time) and subsequently analyze the benefit from this perturbation. This improves the user engagement with other users and items. Thus, the model can learn effective embeddings across the users. To showcase the effectiveness of the proposed counterfactual methodology, we conducted experiments on MovieLens-100K, Amazon Beauty and MovieLens-1M datasets. The experimental results validate the superiority of the proposed approach over the existing techniques.