Attention-aware Next Event Recommendation Strategy for Groups

Abstract

In recent years, event-based social networks (EBSN) have gradually become an effective way for people to choose social events, and how to accurately recommend events to users or groups in need has become an important topic in this field. Next item recommendation can capture users’ dynamic preferences and has been well developed in e-commerce and other fields. However, there are less researches on next event recommendation for groups in EBSN. This paper mainly studies the group-oriented next event recommendation strategy, but due to the dynamic change of group preference, short event life cycle and cold start of new events, it is more difficult to recommend the next event for groups. Firstly, based on the characteristic that group preferences change dynamically over time, the history interaction records of group are divided into each period. Considering the sparsity of the member data due to the period division, which is unfavorable to group preference modeling, an engagement-based ranking strategy is proposed to extract the preferences of core members in the current period, and the attention mechanism is used to fuse them to the group static preferences. Then, the group dynamic preference is obtained by combining the static preference of each period with the attention-based sequential model. Finally, the multi-label classification problem is introduced into the event recommendation, which regards the contexts as labels of the event, and makes the model predict the probability distribution of each context to match the event, so as to alleviate the new event cold start problem. Experimental results verify that the proposed strategy has good performance.

Keywords

• event-based social networks (ebsn); next event recommendation; group recommendation; attention mechanism; multi-label classification