Item Attribute-Aware Contrastive Learning for Sequential Recommendation

Item Attribute-Aware Contrastive Learning for Sequential Recommendation

Sequential recommendation aims to predict users’ next interaction items based on their historical interaction sequences, however, the problem of sparse user behavior and ineffective use of item attribute information makes it difficult to learn high-quality representations of user preferen...

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Bibliographic Details
Main Authors: Bing Yan, Huaxing Wang, Zijie Ouyang, Chao Chen, Yang Xia
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Sequential recommendation
item attribute-aware
contrastive learning
Online Access:https://ieeexplore.ieee.org/document/10177698/

Internet

https://ieeexplore.ieee.org/document/10177698/

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