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SR-DSGA: Session Recommendation for Dual Sequence Based on Graph Neural Network and Multi-Attention
- Source :
- IEEE Access, Vol 12, Pp 109380-109387 (2024)
- Publication Year :
- 2024
- Publisher :
- IEEE, 2024.
-
Abstract
- Session recommender system (SRS) captures user’s sequential features based on historical behavior to predict the next-clicked item. The accuracy of extracting user’s session features directly determines the key performance of SRS. Existing session recommendation methods have two flaws: 1) ignore the complex connections between items, i.e. represent them in a relatively isolated manner; 2) neglect the transition patterns between attributes of items. To address these issues, we propose a novel session recommendation model named SR-DSGA (Session Recommendation for Dual Sequence based on Graph neural network and multi-attention). Firstly, SR-DSGA adopts message passing mechanism in graph neural network to get non-isolated item embedding representations with specific semantic relationship by item-level explicit sequence modeling. Secondly, SR-DSGA exploits the Transformer’s multi-head self-attention mechanism to indirectly obtain item embedding representations in another way through item attribute-level implicit sequence modeling. Therefore, SR-DSGA can help extract the fine-grained features with full sequential patterns even in sparse data scenarios. Finally, soft-attention and time threshold are used to acquire user’s long-term and short-term preferences respectively. Experimental studies on real-world datasets demonstrate the proposed SR-DSGA model outperforms the state-of-the-art benchmark methods.
Details
- Language :
- English
- ISSN :
- 21693536 and 68456034
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0aec6845603429ca14048a602a94198
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2024.3440351