Embedding Implicit User Importance for Group Recommendation

Group recommendations derive from a phenomenon in which people tend to participate in activities together regardless of whether they are online or in reality, which creates real scenarios and promotes the development of group recommendation systems. Different from traditional personalized recommendation methods, which are concerned only with the accuracy of recommendations for individuals, group recommendation is expected to balance the needs of multiple users. Building a proper model for a group of users to improve the quality of a recommended list and to achieve a better recommendation has become a large challenge for group recommendation applications. Existing studies often focus on explicit user characteristics, such as gender, occupation, and social status, to analyze the importance of users for modeling group preferences. However, it is usually difficult to obtain extra user information, especially for ad hoc groups. To this end, we design a novel entropy-based method that extracts users' implicit characteristics from users' historical ratings to obtain the weights of group members. These weights represent user importance so that we can obtain group preferences according to user weights and then model the group decision process to make a recommendation. We evaluate our method for the two metrics of recommendation relevance and overall ratings of recommended items. We compare our method to baselines, and experimental results show that our method achieves a significant improvement in group recommendation performance.