Cross-domain recommendation model based on source domain data augmentation and multi-interest refinement transfer

被引：0
|
作者：
Yin, Yabo [1 ]
Zhu, Xiaofei [1 ]
Liu, Yidan [1 ]
机构：
[1] College of Computer Science and Engineering, Chongqing University of Technology, Chongqing
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2024年 / 58卷 / 08期
关键词：
cold-start problem; cross-domain recommendation; data augmentation; multi-interest extraction; multi-interest refinement transfer;
D O I：
10.3785/j.issn.1008-973X.2024.08.018
中图分类号：
学科分类号：
摘要：
A cross-domain recommendation model that utilizes source domain data augmentation and multi-interest refinement transfer was proposed in order to address the issues of difficulty in modeling interest preferences in cross-domain recommendation tasks caused by the lack of user interaction data in the source domain, as well as the problem of ignored associations between multiple interests. A source-domain data augmentation strategy was introduced, generating a denoised auxiliary sequence for each user in the source domain. Then the sparsity of user interaction data in the source domain was alleviated, and enriched user interest preferences were obtained. The interest extraction and multi-interest refinement transfer were implemented by utilizing the dual sequence multi-interest extraction module and the multi-interest refinement transfer module. Three publicly cross-domain recommendation evaluation tasks were conducted. The proposed model achieved the best performance compared with the best baseline, reducing the average MAE by 22.86% and the average RMSE by 19.65%, which verified the effectiveness of the method. © 2024 Zhejiang University. All rights reserved.
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页码：1717 / 1727
页数：10
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