Mixed Attention Network for Cross-domain Sequential Recommendation
Guanyu Lin Chen Gao Yu Zheng Jianxin Chang Yanan Niu Yang Song Kun Gai Zhiheng Li Depeng Jin Yong Li Meng Wang
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.