Multi-view Group Representation Learning for Location-aware Group Recommendation

2021 ◽  
Author(s):  
Ziyu Lyu ◽  
Min Yang ◽  
Hui Li
Keyword(s):  
Group Representation ◽  
Representation Learning ◽  
Group Recommendation ◽  
Location Aware ◽  
Aware Group

scholarly journals Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

2022 ◽  
Vol 40 (1) ◽  
pp. 1-27
Author(s):  
Lei Guo ◽  
Hongzhi Yin ◽  
Tong Chen ◽  
Xiangliang Zhang ◽  
Kai Zheng
Keyword(s):  
Ad Hoc ◽  
Group Representation ◽  
State Of The Art ◽  
Representation Learning ◽  
Learning Task ◽  
Group Recommendation ◽  
Training Strategy ◽  
Group Members ◽  
Real World Datasets ◽  
Group Similarity

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.

P-LAG: Location-Aware Group Recommendation for Passive Users

2017 ◽  
pp. 242-259 ◽  
Author(s):  
Yuqiu Qian ◽  
Ziyu Lu ◽  
Nikos Mamoulis ◽  
David W. Cheung
Keyword(s):  
Group Recommendation ◽  
Location Aware ◽  
Aware Group

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
Keyword(s):  
Group Representation ◽  
Representation Learning ◽  
Group Recommendation

A Neighbor-Aware Group Recommendation Algorithm

2020 ◽  
pp. 444-452
Author(s):  
Rong Pu ◽  
Bin Wang ◽  
Xiaoxu Song ◽  
Xinqiang Xie ◽  
Jing Qin
Keyword(s):  
Group Recommendation ◽  
Recommendation Algorithm ◽  
Aware Group

scholarly journals TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

2019 ◽  
Vol 94 ◽  
pp. 224-236 ◽  
Author(s):  
Ximeng Wang ◽  
Yun Liu ◽  
Jie Lu ◽  
Fei Xiong ◽  
Guangquan Zhang
Keyword(s):  
Group Recommendation ◽  
Aware Group

Location-Aware Group Preference Queries in Social-Networks

2018 ◽  
pp. 53-67 ◽  
Author(s):  
Ammar Sohail ◽  
Arif Hidayat ◽  
Muhammad Aamir Cheema ◽  
David Taniar
Keyword(s):  
Social Networks ◽  
Group Preference ◽  
Location Aware ◽  
Preference Queries ◽  
Aware Group

An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness

2020 ◽  
Vol 156 ◽  
pp. 102579 ◽  
Author(s):  
Yang Xiao ◽  
Qingqi Pei ◽  
Lina Yao ◽  
Shui Yu ◽  
Lei Bai ◽  
...  
Keyword(s):  
Group Recommendation ◽  
Aware Group
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