Mixed Attention Network for Cross-domain Sequential Recommendation
- URL: http://arxiv.org/abs/2311.08272v1
- Date: Tue, 14 Nov 2023 16:07:16 GMT
- Title: Mixed Attention Network for Cross-domain Sequential Recommendation
- Authors: Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song,
Kun Gai, Zhiheng Li, Depeng Jin, Yong Li, Meng Wang
- Abstract summary: We propose a Mixed Attention Network (MAN) with local and global attention modules to extract the domain-specific and cross-domain information.
Experimental results on two real-world datasets demonstrate the superiority of our proposed model.
- Score: 63.983590953727386
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In modern recommender systems, sequential recommendation leverages
chronological user behaviors to make effective next-item suggestions, which
suffers from data sparsity issues, especially for new users. One promising line
of work is the cross-domain recommendation, which trains models with data
across multiple domains to improve the performance in data-scarce domains.
Recent proposed cross-domain sequential recommendation models such as PiNet and
DASL have a common drawback relying heavily on overlapped users in different
domains, which limits their usage in practical recommender systems. In this
paper, we propose a Mixed Attention Network (MAN) with local and global
attention modules to extract the domain-specific and cross-domain information.
Firstly, we propose a local/global encoding layer to capture the
domain-specific/cross-domain sequential pattern. Then we propose a mixed
attention layer with item similarity attention, sequence-fusion attention, and
group-prototype attention to capture the local/global item similarity, fuse the
local/global item sequence, and extract the user groups across different
domains, respectively. Finally, we propose a local/global prediction layer to
further evolve and combine the domain-specific and cross-domain interests.
Experimental results on two real-world datasets (each with two domains)
demonstrate the superiority of our proposed model. Further study also
illustrates that our proposed method and components are model-agnostic and
effective, respectively. The code and data are available at
https://github.com/Guanyu-Lin/MAN.
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