[论文解读] DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation

Hourun Li, Yifan Wang+5 more|arXiv (Cornell University)|Dec 19, 2024

Recommender Systems and Techniques被引用 5

一句话总结

DisCo 引入一种基于图的解耦对比学习框架，以捕获细粒度的用户意图并过滤源域中无关信息，用于冷启动跨域推荐，减少负迁移。

ABSTRACT

Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains. Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components.

研究动机与目标

• 解决跨域推荐（CDR）中的用户冷启动问题。
• 使用多通道图编码器在每个域中捕获细粒度的用户意图。
• 通过跨域的解耦意图级对比学习来缓解负迁移。

提出的方法

• 使用多通道图编码器在源域和目标域中提取多样化的用户意图。
• 在嵌入空间构建亲和图并应用多步随机游走以获得按意图划分的高阶用户相似性。
• 在域内和域间进行以意图为导向的对比学习，利用用户相似性来细化跨域桥接。
• 结合域间解码器和 EM 式优化，在迁移相关信息的同时保留目标域的特定性。

实验结果

研究问题

• RQ1 解耦的、基于意图的表示能否在冷启动 CDR 中减少负迁移？
• RQ2 高阶（多步）用户相似性是否能改善冷启动用户的跨域桥接？
• RQ3 域内和域间对比损失如何共同影响整体推荐性能？
• RQ4 意图数量和游走参数数量对性能有何影响？

主要发现

• DisCo 在四个基准 CDR 数据集上始终优于最先进的基线方法。
• 消融研究显示跨域解码、意图正交性以及基于亲和图的高阶相似性对性能提升的重要性。
• 通过多步游走实现的高阶用户相似性改善了域内和域间对比学习的结果。
• 案例研究表明，在源域相似性会误导目标域偏好时，DisCo 能缓解负迁移。