Diversified User Recommendation to Avoid Filter Bubbles in Social Media Communities
Hiroki Fujiwara, Soh Yoshida, Mitsuji Muneyasu · 2024
User recommendation systems on social media platforms play an important role in building new friendships and sharing ideas with people who share similar interests. However, highly accurate user recommendation systems can lead to the formation of echo chambers and filter bubbles by presenting only similar users. This reduces the diversity of social relationships and can contribute to the spread of malicious rumors and conspiracy theories. In this paper, we propose a recommendation method that diversifies the user representations by applying the selection of neighborhood information and re-weighting the losses for shared relationships of users, aiming to improve diversity without significantly sacrificing accuracy. Utilizing a dataset from the popular social media platform Twitter (currently X), we demonstrate the effectiveness of this method.