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Item attributes fusion based on contrastive learning for sequential recommendation

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Abstract

Sequential recommendation aims to recommend the next item for a user to interact with by analyzing the user’s historical interaction sequences. Recent studies have utilized attribute information to enhance the performance of recommendation systems. However, these studies can not fully consider the impact of different attributes on item representations. To alleviate this problem, we propose Item Attributes Fusion based on Contrastive Learning for Sequential Recommendation (IAFCL). Specifically, we design an attribute fusion module, which assigns specific weights to various attributes through a constructed item-attribute bipartite graph. Subsequently, it performs a weighted summation of all attributes and their item embeddings to enrich the item representation. In addition, we propose a hybrid loss function that includes contrastive loss function. In constructing the contrastive loss function, we employ a new combined data augmentation strategy to enrich the diversity of contrast samples, thus enabling the model to learn more differentiated representations. Experimental results on three public datasets show that IAFCL outperforms benchmark models.

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Data availability

Amazon Beauty, Sports and Toys: Availability at http://jmcauley.ucsd.edu/data/amazon/.

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Acknowledgements

Thanks to Professor Qin Jiwei for her help and support during the research process.

Funding

This work was supported by the Science Fund for Outstanding Youth of Xinjiang Uygur Autonomous Region under Grant No. 2021D01E14.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xinjiang University, Xinjiang Uygur Autonomous Region, Urumqi, 830017, China

    Donghao zhang, Jiwei Qin, Jie Ma, Zhibin Yang, Daishun Cui & Peichen Ji

  2. Key Laboratory of Signal Detection and Processing, Xinjiang University, Xinjiang Uygur Autonomous Region, Urumqi, 830046, China

    Donghao zhang, Jiwei Qin, Jie Ma, Zhibin Yang, Daishun Cui & Peichen Ji

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  1. Donghao zhang
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  2. Jiwei Qin
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  3. Jie Ma
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  5. Daishun Cui
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Correspondence to Jiwei Qin.

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The authors declare no conflict of interest.

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Not applicable as this study did not involve human participants.

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Communicated by Bing-kun Bao.

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zhang, D., Qin, J., Ma, J. et al. Item attributes fusion based on contrastive learning for sequential recommendation. Multimedia Systems 30, 291 (2024). https://doi.org/10.1007/s00530-024-01486-7

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