Temporal-Aware Neural Networks for Balancing Dynamic Preferences and Long-Term Interests in Recommendation Systems

Abstract

Recommendation systems face the challenge of balancing dynamic short-term preferences with stable long-term interests to deliver personalized and timely recommendations. Traditional methods often treat these aspects separately, leading to suboptimal integration and limited adaptability to evolving user behavior. This paper introduces Temporal-Aware Neural Networks (TANR), a novel framework that leverages a time-aware Transformer architecture to dynamically balance short-term and long-term user preferences. The proposed model incorporates a time decay mechanism within the attention layer to adjust the influence of recent and historical interactions, ensuring a balanced representation of user behavior. Additionally, TANR employs a hybrid training framework combining offline pre-training with online incremental updates, enabling real-time adaptation to user behavior shifts. Extensive experiments on the MovieLens-1M and MIND datasets demonstrate that TANR outperforms state-of-the-art models in both short-term engagement metrics (e.g., Hit Rate, NDCG) and long-term user retention. The results highlight the effectiveness of TANR in capturing temporal dynamics and improving recommendation accuracy, offering a robust solution for modern recommendation systems.