UDA: A user-difference attention for group recommendation
| Autor: | Yujie Zhang, Shuxun Zan, Xiangwu Meng, Pengtao Lv, Yulu Du |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Information Systems and Management
Group (mathematics) Computer science business.industry 05 social sciences Aggregate (data warehouse) 050301 education 02 engineering and technology Recommender system Perceptron Machine learning computer.software_genre Computer Science Applications Theoretical Computer Science Task (project management) Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 0503 education computer Software |
| Zdroj: | Information Sciences. 571:401-417 |
| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2021.04.084 |
| Popis: | Human beings are gregarious by nature, and thus, group activities are indispensable in people’s daily lives. In light of this, group recommendation systems have attracted wide research attention in recent years. The pivotal task of group recommendation is to learn the weights of group members and then aggregate their preferences. Most existing methods only consider a single user and the target item to calculate their weight, which is insufficient to determine the user’s importance in a group. In this study, we emphasize the importance of comparison information. A comparison is an explicit relationship between users. Motivated by our observation and based on attentive group recommendation, we propose a user-difference attention (UDA) model that explicitly simulates the comparisons between group members using relational attention, which is different from previous single user–based models. Each user is compared with all other users under the guidance of the target item, and a multi-layer perceptron is then exploited to add nonlinear transformations . We propose several user relational kernels (URKs) to simulate different types of relations during group decision-making. Extensive experiments were conducted on three public datasets. The results show that UDA significantly exceeds the state-of-the-art competing methods. |
| Databáze: | OpenAIRE |
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