Lighter Sequential Recommendation Algorithm With Time Interval Awareness Augmentation

Abstract

Sequential recommendation models analyze users’ historical interactions to predict the next item they will en gage with. In order to better capture users’ dynamic interest preferences, most existing sequential recommendation models that introduce heterogeneous time intervals lead to increased model complexity, which raises computational costs and training difficulty. This is particularly evident in long sequential data, where the model need to handle a large variety of different time intervals. Additionally, accurately modeling the impact of long time intervals on user behavior remains a significant challenge. To address these issues, we propose a lightweight sequential recommendation algorithm with time interval awareness augmen tation (TALSAN). This model introduces a novel uniform data augmentation operator to improve the distribution of original data samples and employs a time-aware self-attention layer to model user interactions, maintaining the continuity of the original sequence. By integrating temporal context with posi tional features, TALSAN constructs a streamlined self-attention network for predicting user behavior. Comparative testing on datasets such as ML-100K, ML-1M, Amazon Beauty, Amazon Toys, and Amazon Fashion demonstrates the model’s superiority over existing baselines. Our results confirm that TALSAN not only mitigates cold start issues but also enhances the ability to learn user preferences, leading to improved prediction accuracy.