FHAN: Feature-Level Hierarchical Attention Network for Group Event Recommendation
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
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Nanchang, China [email protected], [email protected], [email protected], [email protected] 2 Jiangxi Water Resources Institute, Nanchang, China
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. Keywords: Event-based social network · Hierarchical attention network · Group recommendation · CNN
1 Introduction With the rapid development of social network services, Event-Based Social Networks (EBSN) have emerged and become more and more popular, such as Meetup, Plancast, Douban, etc. Through the platforms, it is easier for people to organize and participate in events or activities such as going to concerts with friends, watching movies with © Springer Nature Switzerland AG 2020 X. Wang et al. (Eds.): APWeb-WAIM 2020, LNCS 12317, pp. 478–492, 2020. https://doi.org/10.1007/978-3-030-60259-8_35
FHAN: Feature-Level Hierarchical Attention Network
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family and attending academic meetings with colleagues, Differing from ordinary social networks, EBSN generally don’t serve for a single user, but rather for groups attending activities together. Therefore, recommending events to groups has become an important task for EBSN. The group recommendation systems usually fuse the preferences of each user in a group when recommending [1], which may occur at different stages of recommendation process. In generally, preference fusion methods can be divided into two categories [6, 8, 23]: recommendation fus
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