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A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

Abstract

Due to the popularity of group activities in social media, group recommendation becomes increasingly significant. It aims to pursue a list of preferred items for a target group. Most deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and users. However, these methods may suffer from data sparsity problem. Except for the interaction between groups and users, there also exist other interactions that may enrich group representation, such as the interaction between groups and items. Such interactions, which take place in the range of a group, form a local view of a certain group. In addition to local information, groups with common interests may also show similar tastes on items. Therefore, group representation can be conducted according to the similarity among groups, which forms a global view of a certain group. In this paper, we propose a novel global and local information fusion neural network (GLIF) model for group recommendation. In GLIF, an attentive neural network (ANN) activates rich interactions among groups, users and items with respect to forming a group′s local representation. Moreover, our model also leverages ANN to obtain a group′s global representation based on the similarity among different groups. Then, it fuses global and local representations based on attention mechanism to form a group′s comprehensive representation. Finally, group recommendation is conducted under neural collaborative filtering (NCF) framework. Extensive experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61872363 and 61672507), Natural Foundation of Beijing Municipal Commission of Education, China (No. 21JD0044), National Key Research and Development Program of China (No. 2016YFB 0401202), and the Research and Development Fund of Institute of Automation, Chinese Academy of Sciences, China (No. Y9J2FZ0801).

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Correspondence to Dan-Li Wang.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Song Zhang received the B. Sc. degree in software engineering from Xiamen University, China in 2019. He is currently a master student in computer technology at University of Chinese Academy of Sciences, China.

His research interests include data mining and machine learning.

Nan Zheng received the Ph.D. degree in computer application from Institute of Automation, Chinese Academy of Sciences, China in 2012. She is currently an associate professor at State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China. She was a visiting scholar at University of California, Berkeley, USA from 2018 to 2019.

Her research interests include data mining and machine learning.

Dan-Li Wang received the Ph.D. degree in computer application from Beihang University, China in 1999. She is currently a researcher at State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China.

Her research interests include complex systems, metasynthesis, group intelligence, human-computer interaction, psychosomatic computation, data mining and machine learning.

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Zhang, S., Zheng, N. & Wang, DL. A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation. Mach. Intell. Res. 19, 331–346 (2022). https://doi.org/10.1007/s11633-022-1336-1

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Keywords

  • Group recommendation
  • attentive neural network (ANN)
  • global information
  • local information
  • recommender system