Multiple Attention Group Event Recommendation with Fine-Grained Features

Event-based social network is a novel platform where users establish social relationships and realize interest matching through participation in offline events. However, event recommendation faces severe challenges, including extreme data sparsity due to the short life cycles of events, and the dynamic interplay between individual and group preferences on fine-grained features. Existing methods ignore the users’ diverse and personalized preferences across different event features such as venue and organizer. We first conduct a data analysis to argue that not only do users themselves have different preferences for different features, but more importantly, these preferences dynamically influence the behavior of different groups they belong to. Therefore, in this work, we propose a novel Multiple Attention Group Event Recommendation (MAGER) framework based on Neural Collaborative Filtering to address these challenges. MAGER first employs fine-grained feature attention to generate personalized event representations, and then dynamically aggregates member preferences and influences through a group-level attention mechanism. More importantly, a heterogeneous attention structure integrates these learning modules, enabling generation of more accurate representations of event, users, and groups. We conduct extensive experiments on three-real world datasets, the experimental results show that MAGER achieves substantial improvements in user and group recommendations effectiveness compared to baselines in terms of HR@K and NDCG@K. Specifically, HR@5 improvement of 3.63–13.07%; NDCG@K improvement of 5.63–18.55% for user recommendation, and HR@5 improvement of 2.05–19.02%; NDCG@K improvement of 2.51–36.12% for group recommendation on three datasets.