GAT4Rec: Sequential Recommendation with a Gated Recurrent Unit and Transformers

Huaiwen He, Xiangdong Yang, Feng Huang, Feng Yi and Shangsong Liang ()
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Huaiwen He: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhognshan 528400, China
Xiangdong Yang: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhognshan 528400, China
Feng Huang: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhognshan 528400, China
Feng Yi: School of Computer, Zhongshan Institute, University of Electronic Science and Technology of China, Zhognshan 528400, China
Shangsong Liang: School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China

Mathematics, 2024, vol. 12, issue 14, 1-23

Abstract: Capturing long-term dependency from historical behaviors is the key to the success of sequential recommendation; however, existing methods focus on extracting global sequential information while neglecting to obtain deep representations from subsequences. Previous research has revealed that the restricted inter-item transfer is fundamental to sequential modeling, and some potential substructures of sequences can help models learn more effective long-term dependency compared to the whole sequence. To automatically find better subsequences and perform efficient learning, we propose a sequential recommendation model with a gated recurrent unit and Transformers, abbreviated as GAT4Rec, which employs Transformers with shared parameters across layers to model users’ historical interaction sequences. The representation learned by the gated recurrent unit is used as the gating signal to identify the optimal substructure in user sequences. The fused representation of the subsequence and edge information is extracted by the encoding layer to make the corresponding recommendations. Experimental results on four well-known publicly available datasets demonstrate that our GAT4Rec model outperforms other recommendation models, achieving performance improvements of 5.77%, 1.35%, 11.58%, and 1.79% in the normalized discounted cumulative gain metric (NDCG@10), respectively.

Keywords: sequential recommendation; transformer; gated recurrent unit (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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