Does user interest matter? Exploring the impact of ignoring user interests in recommendations

Abstract

User interests have long been a critical factor in recommender systems, serving as a key criterion for recommendations. Existing interest-based recommendation prioritize matching items to users’ interests, often overlooking the importance of the intrinsic characteristics of items. This can lead to recommendations that, while aligned with user interests, fail to address the user’s specific preferences for item intrinsic characteristics, reducing satisfaction and trust in the system. In this paper, we propose an interest disentangling recommendation algorithm (IDG). During the model-training phase, user interactions with items are disentangled into user preference for items’ intrinsic characteristics and interest groups associated with those items. During the prediction phase, downplay the user’s preferences for items’ interest groups and focus more on their preferences for the items’ intrinsic characteristics. Extensive experiments show that, on average, IDG outperforms the ten baselines by 15.9%, 34.2%, 25% and 32.8% in terms of HR@20, NDCG@20, PRE@20, and ILS@20, respectively, across three real datasets. Further experiments show that items recommended by the IDG algorithm are more concentrated within the same interest groups. However, IDG effectively enhances the diversity of items within the recommendation list, quantified by item similarity.