arXiv cs.AI· Abdulaziz Samra, Nikita Sukhorukov, Alexey Grishanov, Alexey Vasilev, Evgeny Frolov·· 4 小时前AI 评分34
跨域序列推荐新框架：通过表示级用户迁移实现跨域信息传递
Cross-Domain Sequential Recommendation with Representation-Level User Transfer
AI 导读
研究者提出一种跨域序列推荐框架，仅通过学习到的用户表示在源域与目标域之间传递信息，不共享原始交互数据或物品嵌入。框架引入轻量级 adapter，将源域用户表示投影到目标域嵌入空间并与目标交互序列融合；当源域数据缺失时可退化为仅目标域的序列推荐器。两组实验均显示对共享用户和非共享用户均有持续性能提升，论文已被 CIKM'26 全文赛道接收。
正文 · 原文
Abstract:Cross-domain sequential recommendation aims to improve next-item prediction in a target domain by leveraging user behavior from auxiliary source domains. However, real-world deployment faces two critical challenges: business constraints that isolate interaction data across domains, and user heterogeneity, where many users lack cross-domain activity. This makes existing cross-domain methods difficult to apply. In this paper, we propose a simple and practical framework for cross-domain sequential recommendations that transfers information exclusively through learned user representations, without sharing raw interaction data or item embeddings across domains. We introduce a lightweight adapter that projects the source-domain user representation into the target-domain embedding space and integrates it with the target interaction sequence. Importantly, when source data is unavailable (i.e., the user observed only in the target domain), the model seamlessly defaults to a robust target-only sequential recommender, retaining benefits from joint training across all users. Extensive experiments were conducted under two setups; one targets benchmarking against previous baselines, while the other focuses on realistic and scalable conditions. Both sets of experiments demonstrate consistent performance gains for shared and unshared users.
| Comments: | Accepted in CIKM'26 - full paper track |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC) |
| MSC classes: | H.3.3, I.2.6 |
| Cite as: | arXiv:2610.35550 [cs.IR] |
| (or arXiv:2610.35550v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35550 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1145/3799682.3841135
DOI(s) linking to related resources |
Submission history
From: Abdulaziz Samra [view email]
[v1]
Mon, 26 Oct 2026 14:55:24 UTC (300 KB)
来源：arXiv cs.AI · arxiv.org