User-controllable Recommendation Against Filter Bubbles
Wenjie Wang, Fuli Feng, Liqiang Nie, Tat‐Seng Chua · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Recommender systems usually face the issue of filter bubbles: over-recommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention.