Neighbor interaction-based personalised transfer for cross-domain recommendation

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kelei Sun, Yingying Wang, Mengqi He, Huaping Zhou, Shunxiang Zhang
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引用次数: 0

Abstract

Mapping-based cross-domain recommendation (CDR) can effectively tackle the cold-start problem in traditional recommender systems. However, existing mapping-based CDR methods ignore data-sparse users in the source domain, which may impact the transfer efficiency of their preferences. To this end, this paper proposes a novel method named Neighbor Interaction-based Personalized Transfer for Cross-Domain Recommendation (NIPT-CDR). This proposed method mainly contains two modules: (i) an intra-domain item supplementing module and (ii) a personalised feature transfer module. The first module introduces neighbour interactions to supplement the potential missing preferences for each source domain user, particularly for those with limited observed interactions. This approach comprehensively captures the preferences of all users. The second module develops an attention mechanism to guide the knowledge transfer process selectively. Moreover, a meta-network based on users' transferable features is trained to construct personalised mapping functions for each user. The experimental results on two real-world datasets show that the proposed NIPT-CDR method achieves significant performance improvements compared to seven baseline models. The proposed model can provide more accurate and personalised recommendation services for cold-start users.
基于邻居交互的跨域推荐个性化转移
基于映射的跨域推荐(CDR)可以有效地解决传统推荐系统的冷启动问题。然而,现有的基于映射的话单方法忽略了源域中数据稀疏的用户,这可能会影响用户偏好的传递效率。为此,本文提出了一种基于邻居交互的跨域推荐个性化传输方法(npt - cdr)。该方法主要包含两个模块:(1)域内项目补充模块和(2)个性化特征传递模块。第一个模块引入邻居交互,以补充每个源域用户潜在的缺失偏好,特别是对于那些观察到的交互有限的用户。这种方法全面地捕获了所有用户的偏好。第二个模块发展了一个注意力机制来有选择地引导知识转移过程。此外,基于用户可转移特征的元网络被训练为每个用户构建个性化的映射函数。在两个真实数据集上的实验结果表明,与七个基线模型相比,所提出的npt - cdr方法取得了显著的性能改进。该模型可以为冷启动用户提供更加精准、个性化的推荐服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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