TCSE: Time-aware Causal Disentanglement with Semantic Knowledge Enhancement for Personalized Mobile Application Recommendation

In recent years, causal disentanglement, which seeks to separate user interest from social conformity, has attracted growing attention for its potential to mitigate popularity bias in recommender systems. The key to completing this task is accurately modeling the causal drivers of user interactions with items. Due to the rapid development of causal inference, most state-of-the-art (SOTA) approaches aim to learn representations of interest and conformity from interaction data. Despite their accomplishments, two limitations remain. First, most existing approaches rely on global random sampling, which overlooks the time-varying nature of popularity and leads to temporal confounding bias. Second, most ID-based approaches suffer from semantic sparsity, neglecting the rich textual metadata crucial to understanding the intrinsic content of mobile apps (short for applications). To this end, we propose a temporal causal semantic enhancement (TCSE) framework to robustly disentangle user interest from conformity by integrating time-aware sampling and semantic reasoning. More specifically, we introduce a semantic knowledge enhancement module to augment mobile app representations with generalized semantics extracted from large language models. Then, we design a temporal causal sampling strategy to construct locally unbiased interaction pairs within the same temporal window, effectively capturing dynamic popularity shifts. Extensive experiments on two real-world large-scale datasets indicate that TCSE can outperform SOTA models in both recommendation accuracy and debiasing effectiveness. An ablation study also demonstrates the necessity of TCSE’s semantic and temporal components.