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IPSRM: An intent perceived sequential recommendation model
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DOI：10.1016/j.jksuci.2024.102206.png)
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Abstract
En 中文
Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process.
Keywords:
Sequential recommendation
Contrastive learning
Self-supervised learning
Mutual information
Latent factor modeling
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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