Causal Disentanglement for Tackling Popularity Bias in Sequential Recommendation

An-An Liu, Yadong Zhao, Xin Wen, Rihao Chang, Weizhi Nie

Abstract: Recommender systems typically exhibit severe popularity bias, with a few highly popular items receiving excessive exposure. Most existing studies tackle this bias in static settings. However, they neglect the dynamic nature of real-world recommendation scenarios and lack a thorough analysis into the root causes of bias, which makes it challenging to accurately model and mitigate the dynamically changing popularity bias and capture genuine user preferences. To this end, we propose a causal disentanglement sequential recommendation model (CDSRec) based on time series analysis and hidden variable separation. Our model leverages Markov chains to analyze historical interaction data within sequential recommendations, capturing the dynamic variations of item popularity and user preferences. Employing causal inference, we disentangle the potential factors implicated in popularity bias. Specifically, user–item interactions are primarily driven by personalized demands and item popularity. Through empirical analysis from a temporal perspective, we reveal that popularity has both positive and negative impacts, and attribute them to stable intrinsic quality factors and dynamic external interference factors. We construct a causal directed acyclic graph to elucidate the temporal correlations among different factors. Subsequently, we utilize historical interaction sequences and item-related attributes as auxiliary information to explicitly disentangle these factors as hidden variables. By reformulating the objective function to optimize the sequential VAE framework, our model effectively mitigates the negative impact of external interference factors. Extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model.

External IDs:dblp:journals/tai/LiuZWCN26