Capturing Dynamic User Preferences: A Recommendation System Model with Non-Linear Forgetting and Evolving Topics

Though recommendation systems can help users save time while shopping online, their performance is significantly limited by sparse user data and the inability to capture temporal dynamics of user preferences, such as interest forgetting and topic evolution in reviews. Existing studies primarily focus on static user–item interactions or partial temporal signals (e.g., rating timestamps) but fail to comprehensively model two critical aspects: the non-linear decay of user interests over time, where users gradually forget historical preferences, and the semantic evolution of review topics, which reflects implicit shifts in user preferences across different periods. To address these limitations, we propose a Temporal Dynamic Latent Review-aware Preference Model with Matrix Factorization. Our model integrates an adaptive forgetting-weight function to simulate users’ interest decay and a multi-interval latent topic model to extract evolving preference features from review semantics. Specifically, we design a joint optimization framework that dynamically weights user ratings based on temporal forgetting patterns and decomposes review texts into latent topic factors to alleviate data sparsity. Finally, the experiments employ five baseline methods on six datasets to test the recommendation performance, validating its effectiveness in tracking users’ temporal interest drift and improving recommendation accuracy.