FHAN: Feature-Level Hierarchical Attention Network for Group Event Recommendation

Abstract

Recommending events to groups is different from to single-user in event-based social networks (EBSN), which involves various complex factors. Generally, group recommendation methods are either based on recommendation fusion or model fusion. However, most existing methods neglect the fact that user preferences change over time. Moreover, they believe that the weights of different factors that affect group decision-making are fixed in different periods. Recently, there are a few works using the attention mechanism for group recommendation. Although they take into account the dynamic variability of user preferences and the dynamic adjustment of user features weights, they haven’t discussed more features of groups and events affecting group decision-making. To this end, we propose a novel Feature-level Hierarchical Attention Network (FHAN) for group event recommendation for EBSN. Specifically, group decision-making factors are divided into group-feature factors and event-feature factors, which are integrated into a two-layer attention network. The first attention layer is constructed to learn the influence weights of words of group topics and event topics, which generates better thematic features. The second attention layer is built to learn the weights of group-feature factors and event-feature factors affecting group decision-making, which results in better comprehensive representation of groups and events. All influence weights of different features in the model can be dynamically adjusted over time. Finally, we evaluate the suggested model on three real-world datasets. Extensive experimental results show that FHAN outperforms the state-of-the-art approaches.