Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2019.2936475
Title: Social-enhanced Attentive Group Recommendation
Authors: Da Cao
Xiangnan He 
Lianhai Miao
Guangyi Xiao
Hao Chen 
Richang Hong 
Keywords: Group Recommendation
Attention Network
Social Followee Information
Neural Collaborative Filtering
Issue Date: 20-Aug-2019
Publisher: IEEE
Citation: Da Cao, Xiangnan He, Lianhai Miao, Guangyi Xiao, Hao Chen, Richang Hong (2019-08-20). Social-enhanced Attentive Group Recommendation. IEEE Transactions on Knowledge and Data Engineeringÿ. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2019.2936475
Abstract: With the proliferation of social networks, group activities have become an essential ingredient of our daily life. A growing number of users share their group activities online and invite their friends to join in. This imposes the need of an in-depth study on the group recommendation task, i.e., recommending items to a group of users. In this article, we devise neural network-based solutions by utilizing the recent developments of attention network and neural collaborative filtering. First of all, we adopt an attention network to form the representation of a group by aggregating the group members' embeddings, which allows the attention weights of group members to be dynamically learnt from data. Secondly, the social followee information is incorporated via another attention network to enhance the representation of individual user, which is helpful to capture users' personal preferences. Thirdly, considering that many online group systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, the recommendation for groups and users can be mutually reinforced. Extensive experiments on the scope of both macro-level performance comparison and micro-level analyses justify the effectiveness and rationality of our proposed approaches.
Source Title: IEEE Transactions on Knowledge and Data Engineeringÿ
URI: https://scholarbank.nus.edu.sg/handle/10635/168413
ISSN: 10414347
DOI: 10.1109/TKDE.2019.2936475
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