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GroupMO: a memory-augmented meta-optimized model for group recommendation

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Abstract

Group recommendation aims to suggest desired items for a group of users. Existing methods can achieve inspiring results in predicting the group preferences in data-rich groups. However, they could be ineffective in supporting cold-start groups due to their sparsity interactions, which prevents the model from understanding their intent. Although cold-start groups can be alleviated by meta-learning, we cannot apply it by using the same initialization for all groups due to their varying preferences. To tackle this problem, this paper proposes a memory-augmented meta-optimized model for group recommendation, namely GroupMO. Specifically, we adopt a clustering method to assemble the groups with similar profiles into the same cluster and design a representative group profile memory to guide the preliminary initialization of group embedding network for each group by utilizing those clusters. Besides, we also design a group shared preference memory to guide the prediction network initialization at a more refined granularity level for different groups, so that the shared knowledge can be better transferred to groups with similar preferences. Moreover, we incorporate those two memories to optimize the meta-learning process. Finally, extensive experiments on two real-world datasets demonstrate the superiority of our model.

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Availability of Data and Materials

Both datasets analysed during the current study are available in the following public domain resources:Mafengwo and CAMRa20: https://github.com/FDUDSDE/WWW2023ConsRec

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Funding

This work is supported by the National Natural Science Foundation of China (No. 62272334).

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China

    Jiawei Hong, Wen Yang, Pingfu Chao & Junhua Fang

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  1. Jiawei Hong
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  2. Wen Yang
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  4. Junhua Fang
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Contributions

Wen Yang and Jiawei Hong wrote the main manuscript text. Jiawei Hong, Junhua Fang, Pingfu Chao participated in model design and technical discussion. All contributing authors reviewed the manuscript.

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Correspondence to Wen Yang, Pingfu Chao or Junhua Fang.

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Jiawei Hong and Wen Yang as co-first authors.

This article belongs to the Topical Collection: Special Issue on Advancing recommendation systems with foundation models Guest Editors: Kai Zheng, Renhe Jiang, and Ryosuke Shibasaki

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Hong, J., Yang, W., Chao, P. et al. GroupMO: a memory-augmented meta-optimized model for group recommendation. World Wide Web 27, 27 (2024). https://doi.org/10.1007/s11280-024-01267-2

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  • DOI: https://doi.org/10.1007/s11280-024-01267-2

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