An Adaptive Social Relationship Incorporated Model for Enhancing Diversity in Sequential Recommendations

Contemporary research on sequential recommender systems primarily focuses on optimizing accuracy, but recommendation diversity is often overlooked. This concept involves recommending diverse items to users, providing them with broader choices and preventing content from being too similar. There is typically a trade-off between accuracy and diversity, as increasing diversity can reduce accuracy by recommending items that are less similar to a user's previous preferences. While trust, as auxiliary information, is often used to increase recommendation accuracy, the potential of leveraging trust to enhance the diversity of sequential recommendations remains largely unexplored. In this paper, we propose a trust-aware recommendation method aimed at enhancing diversity in sequential recommendations while maintaining accuracy. In this paper, we propose a trust-aware recommendation method aimed at enhancing diversity in sequential recommendations while minimizing any impact on accuracy. Experiments on two public datasets from various domains demonstrate that the proposed method outperforms baseline methods in both overall and individual diversity while maintaining a comparable level of accuracy.