PRUC & Play: Probabilistic Residual User Clustering for Recommender Systems

Wenyuan Wang, Yusong Zhao, Zihao Xu, Hengyi Wang, Shreya Venugopal, Desmond Lobo, Chengzhi Mao, Qi Xu, Zhigang Hua, Yan Xie, Bo Long, Shuang Yang, Hao Wang

Keywords: Recommendation System, Causal Inference, Bayesian Deep Learning

TL;DR: A Bayesian recommendation system that leverages causal inference to enhance performance while maintaining interpretability.

Abstract:

Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically interpret their outputs and enhance their recommendation capabilities. To address this problem, we propose *Probabilistic Residual User Clustering (PRUC)*, a causal Bayesian recommendation model based on user clustering. Specifically, we address this problem by (1) dividing users into clusters in an unsupervised manner and identifying causal confounders that influence latent variables, (2) developing sub-models for each confounder given the observable variables, and (3) generating recommendations by aggregating the rating residuals under each confounder using do-calculus.
Experiments demonstrate that our *plug-and-play* PRUC is compatible with various base DL recommender systems, significantly improving their performance while automatically discovering meaningful user clusters.

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

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Submission Number: 3282