Abstract：In order to improve the recommendation effect of social events in Event-based Social Network (EBSN), a personalized social event recommendation method combining historical behaviors and social relationships of users was proposed. Firstly, deep learning technology was used to build a user model from two aspects:the user's historical behaviors and the potential social relationships between users. Then, when modeling user preferences, the negative vector representation of user preferences was introduced, and the attention weight layer was used to assign different weights to different events in the user's historical behaviors and different friends in the user's social relationships according to different candidate recommendation events, at the same time, the various characteristics of events and groups were considered. Finally, a lot of experiments were carried out on the real datasets. Experimental results show that this personalized social event recommendation method is better than the comparative Deep User Modeling framework for Event Recommendation (DUMER) and DIN (Deep Interest Network) model combined with attention mechanism in terms of Hits Ratio (HR), Normalized Discounted Cumulative Gain (NDCG) and Mean Reciprocal Rank (MRR) evaluation indicators.
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2021, Vol. 41 
