Socially-driven multi-interaction attentive group representation learning for group recommendation

2021 ◽  
Vol 145 ◽  
pp. 74-80
Author(s):  
Peipei Wang ◽  
Lin Li ◽  
Ru Wang ◽  
Guandong Xu ◽  
Jianwei Zhang
2022 ◽  
Vol 40 (1) ◽  
pp. 1-27
Author(s):  
Lei Guo ◽  
Hongzhi Yin ◽  
Tong Chen ◽  
Xiangliang Zhang ◽  
Kai Zheng

Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.


Author(s):  
Charles S. Maier ◽  
Charles S. Maier

The author, one of the most prominent contemporary scholars of European history, published this, his first book, in 1975. Based on extensive archival research, the book examines how European societies progressed from a moment of social vulnerability to one of political and economic stabilization. Arguing that a common trajectory calls for a multi country analysis, the book provides a comparative history of three European nations—France, Germany, and Italy—and argues that they did not simply return to a prewar status quo, but achieved a new balance of state authority and interest group representation. While most previous accounts presented the decade as a prelude to the Depression and dictatorships, the author suggests that the stabilization of the 1920s, vulnerable as it was, foreshadowed the more enduring political stability achieved after World War II. The immense and ambitious scope of this book, its ability to follow diverse histories in detail, and its effort to explain stabilization—and not just revolution or breakdown—have made it a classic of European history.


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