在元表征上转移因果机制,实现目标未知的跨域推荐

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shengyu Zhang, Qiaowei Miao, Ping Nie, Mengze Li, Zhengyu Chen, Fuli Feng, Kun Kuang, Fei Wu
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引用次数: 0

摘要

为了解决推荐系统中普遍存在的数据稀缺问题,我们对正在蓬勃发展的非重叠跨域推荐领域进行了深入研究,这种技术可以促进跨域交互知识的传递,而不需要域间用户/物品的对应关系。现有方法主要依赖用户评论和物品标签等辅助信息来建立域间连接,但由于隐私和商业限制,这些资源可能无法访问。为了解决这些局限性,我们的研究对目标未知跨域推荐进行了深入探讨,以应对在源域训练阶段缺乏目标域信息这一独特挑战。我们说明了目标未知跨域推荐固有的两个关键障碍:由于用户/项目对应关系或侧面信息不足而缺乏跨域桥梁,以及在面对跨域分布变化时源域训练偏差的潜在隐患。为了克服这些障碍,我们提出了 CMCDR 框架,这是一种利用从元用户/项目表征中提取的因果机制的新方法。CMCDR 框架采用矢量量化编码器-解码器架构,实现了用户/物品特征的分离。我们认为,从用户/项目特征(即元表征)而不是原始用户和项目中,更容易辨别出领域可转移知识。利用这些元表征,我们的 CMCDR 框架巧妙地纳入了注意力驱动预测器,该预测器近似于以因果理论为基础的前门调整方法。这一尖端策略有效地减轻了源域训练偏差,并增强了针对分布变化的泛化能力。广泛的实验证明了 CMCDR 在目标未知的跨域推荐中的实证有效性和合理性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transferring Causal Mechanism over Meta-representations for Target-unknown Cross-domain Recommendation

Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user/item correspondence. Existing approaches have predominantly depended on auxiliary information, such as user reviews and item tags, to establish inter-domain connectivity, but these resources may become inaccessible due to privacy and commercial constraints.

To address these limitations, our study introduces an in-depth exploration of Target-unknown Cross-domain Recommendation, which contends with the distinct challenge of lacking target domain information during the training phase in the source domain. We illustrate two critical obstacles inherent to Target-unknown CDR: the lack of an inter-domain bridge due to insufficient user/item correspondence or side information, and the potential pitfalls of source-domain training biases when confronting distribution shifts across domains. To surmount these obstacles, we propose the CMCDR framework, a novel approach that leverages causal mechanisms extracted from meta-user/item representations. The CMCDR framework employs a vector-quantized encoder-decoder architecture, enabling the disentanglement of user/item characteristics. We posit that domain-transferable knowledge is more readily discernible from user/item characteristics, i.e., the meta-representations, rather than raw users and items. Capitalizing on these meta-representations, our CMCDR framework adeptly incorporates an attention-driven predictor that approximates the front-door adjustment method grounded in causal theory. This cutting-edge strategy effectively mitigates source-domain training biases and enhances generalization capabilities against distribution shifts. Extensive experiments demonstrate the empirical effectiveness and the rationality of CMCDR for target-unknown cross-domain recommendation.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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