Multi-Interest Aware Sequential Recommender System Based on Contrastive Learning

Zhao Rongmei; Sun Siyu; Yan Fanli; Peng Jian; Ju Shenggen

doi:10.7544/issn1000-1239.202330622

Journal of Computer Research and Development > 2024 > 61(7): 1730-1740. > DOI: 10.7544/issn1000-1239.202330622

Zhao Rongmei, Sun Siyu, Yan Fanli, Peng Jian, Ju Shenggen. Multi-Interest Aware Sequential Recommender System Based on Contrastive Learning[J]. Journal of Computer Research and Development, 2024, 61(7): 1730-1740. DOI: 10.7544/issn1000-1239.202330622

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Multi-Interest Aware Sequential Recommender System Based on Contrastive Learning

College of Computer Science, Sichuan University, Chengdu 610065

Funds: This work was supported by the National Natural Science Foundation of China (62137001).

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Author Bio:
Zhao Rongmei: born in 1996. PhD candidate. Her main research interests include recommendation system and data mining

Sun Siyu: born in 2000. Master candidate. Her main research interests include recommendation system, data mining, and natural language processing

Yan Fanli: born in 1999. Master candidate. His main research interests include recommendation system and natural language processing

Peng Jian: born in 1970. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include Internet of things and medical artificial intelligence

Ju Shenggen: born in 1970. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include data mining, natural language processing, and knowledge graph
Received Date: July 30, 2023
Revised Date: November 30, 2023
Available Online: March 03, 2024

Graphical Abstract

Abstract

Abstract

Recent advancements in the field of sequential recommender have focused on refining user interests through various methods, such as clustering historical interactions or utilizing graph convolutional neural networks to capture multi-level correlations among interactions. However, while these approaches have significantly advanced the field, they often overlook the interactions between users with similar behavioral patterns and the impact of irregular time intervals within interaction sequences on user interests. Based on the above problems, a multi-interest aware sequential recommender model (MIRec) based on contrastive learning is proposed. This model takes into account both local preference information, including item dependence and location dependence within a sequence, and global preference information obtained through a graph information aggregation mechanism among similar users. The user representations, which incorporate both local and global preferences, are fed into a capsule network to learn multi-interest representations within the user interaction sequence. Subsequently, the user’s historical interaction sequences are brought closer to enhanced interaction sequences through contrastive learning. This process results in the generation of a user’s multi-interest representation that is insensitive to time intervals, ultimately leading to more accurate recommendations for users. The effectiveness of this model is verified on two real datasets, and the experimental results verify the effectiveness of the model.
- multiple interests,
- global preference,
- local preference,
- capsule network,
- sequential recommendation

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References (36)

References

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