Hybrid Deep Framework for Group Event Recommendation

Abstract

Group recommender systems suggesting items for a group of users have received many attentions recently. Some aggregation-based and model-based group recommendation methods have been proposed. However, the cold-start problem in group recommendation has not been well studied, which limits the application of group recommendation in many important domains, such as recommending offline events for a group of users. In this paper, we propose a new hybrid deep framework to solve cold-start problem of group event recommendation. Our framework incorporates multiple Restricted Boltzmann Machines (RBM) and conditional RBM. The former extracts high latent group preference from user feedback and group feedback. The latter obtains latent event features based on contextual information, such as location and organizer of events. Thus, the hybrid deep framework can utilize user feedback and contextual information of events to overcome cold-start problem. We conduct exhaustive experiments on two real-world datasets and the results show that our proposed framework outperforms the baseline group recommendation methods and alleviates the cold-start problem of group event recommendation effectively.

Keywords

• Group recommendation
• hybrid recommendation
• cold-start problem
• restricted Boltzmann machines
• deep belief networks