Examining the Impact of Multi-Objective Recommender Systems on Providers Bias

Recommender systems are designed to help customers in finding their personalized content. However, biases in recommender systems can potentially exacerbate over time. Multi-objective recommender system (MORS) algorithms aim to alleviate bias while maintaining the accuracy of recommendation lists. While these algorithms effectively address item-side fairness, provider-side fairness often remains neglected. This study investigates the impact of MORS algorithms, leveraging evolutionary techniques to mitigate popularity bias on the item-side, on providers' fairness. Our findings reveal that baseline algorithms can adversely affect providers' fairness. Moreover, it is demonstrated that evolutionary algorithms, specifically those introducing less popular items to the initial population of their algorithms, exhibit superior performance compared to other MORS algorithms in enhancing providers' fairness. This research sheds light on the crucial role MORS algorithms, particularly those employing evolutionary approaches, can play in mitigating bias and promoting fairness for both users and providers in recommender systems.