Social recommendation systems face the problem of social influence bias, which can lead to an overemphasis on recommending items that friends have interacted with. Addressing this problem is crucial, and existing methods often rely on techniques such as weight adjustment or leveraging unbiased data to eliminate this bias. However, we argue that not all biases are detrimental, i.e., some items recommended by friends may align with the user's interests. Blindly eliminating such biases could undermine these positive effects, potentially diminishing recommendation accuracy. In this paper, we propose a Causal Disentanglement-based framework for Regulating Social influence Bias in social recommendation, named CDRSB, to improve recommendation performance. From the perspective of causal inference, we find that the user social network could be regarded as a confounder between the user and item embeddings (treatment) and ratings (outcome). Due to the presence of this social network confounder, two paths exist from user and item embeddings to ratings: a non-causal social influence path and a causal interest path. Building upon this insight, we propose a disentangled encoder that focuses on disentangling user and item embeddings into interest and social influence embeddings. Mutual information-based objectives are designed to enhance the distinctiveness of these disentangled embeddings, eliminating redundant information. Additionally, a regulatory decoder that employs a weight calculation module to dynamically learn the weights of social influence embeddings for effectively regulating social influence bias has been designed. Experimental results on four large-scale real-world datasets Ciao, Epinions, Dianping, and Douban book demonstrate the effectiveness of CDRSB compared to state-of-the-art baselines.

翻译：社交推荐系统面临社会影响偏差问题，该偏差可能导致过度推荐好友已交互的物品。解决这一问题至关重要，现有方法通常依赖权重调整或利用无偏数据消除此类偏差。然而，我们认为并非所有偏差都有害——即好友推荐的某些物品可能与用户兴趣相符。盲目消除这类偏差可能削弱其积极影响，进而降低推荐精度。本文提出一种基于因果解耦的社交推荐社会影响偏差调控框架CDRSB，旨在提升推荐性能。从因果推断视角出发，我们发现用户社交网络可视为用户与物品嵌入（处理变量）及评分（结果变量）之间的混杂因子。由于该社交网络混杂因子的存在，从用户与物品嵌入到评分存在两条路径：非因果的社会影响路径与因果的兴趣路径。基于此发现，我们提出一种解耦编码器，专注于将用户与物品嵌入解耦为兴趣嵌入与社会影响嵌入。我们设计了基于互信息的目标函数以增强这些解耦嵌入的区分度，消除冗余信息。此外，还设计了一种调控解码器，通过权重计算模块动态学习社会影响嵌入的权重，从而有效调节社会影响偏差。在Ciao、Epinions、大众点评和豆瓣图书四个大规模真实数据集上的实验结果表明，CDRSB相较于当前最优基线模型具有显著优势。