Bayesian inductive learning in group recommendations for seen and unseen groups

Article Computer Science, Information Systems

Multi-view group representation learning for location-aware group recommendation

Ziyu Lyu et al.

Summary: This manuscript proposes a Multi-view Group Representation Learning framework for location-aware group recommendation, which effectively addresses the complexities of group decision making, data sparsity, and cold-start problems. Experiments on Foursquare and Plancast datasets demonstrate that the proposed method significantly outperforms existing approaches.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

DGC: Dynamic group behavior modeling that utilizes context information for group recommendation

Hyun Ji Jeong et al.

Summary: This paper introduces a novel group recommendation framework called DGC, which utilizes dynamic group behavior modeling and context information. By applying context information using semi-supervised learning, the framework significantly improves performance in group recommendation.

KNOWLEDGE-BASED SYSTEMS (2021)

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Article Computer Science, Artificial Intelligence

Social-Enhanced Attentive Group Recommendation

Da Cao et al.

Summary: With the rise of social networks, group activities have become an essential part of daily life. However, issues in group recommendation persist due to factors such as group member weights, social followee information, and user-item interactions. This study proposes neural network-based solutions to address these challenges effectively.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2021)

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Article Computer Science, Information Systems

UDA: A user-difference attention for group recommendation

Shuxun Zan et al.

Summary: Group recommendation systems have gained significant research attention in recent years, with the focus on learning the weights of group members and aggregating their preferences. This study emphasizes the importance of comparison information and proposes a user-difference attention model to explicitly simulate comparisons between group members.

INFORMATION SCIENCES (2021)

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Proceedings Paper Computer Science, Information Systems