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.

本文提出了一种基于因果解缠的社交推荐系统，名为 CDRSB，用于改进推荐性能。实验结果表明，与现有方法相比，CDRSB 在四个大规模真实世界数据集上的效果更好，解决了社交推荐系统面临的社交影响偏差问题。