LAMEE: a light all-MLP framework for time series prediction empowering recommendations

Abstract

Exogenous variables, unrelated to the recommendation system itself, can significantly enhance its performance. Therefore, integrating these time-evolving exogenous variables into a time series and conducting time series predictions can maximize the potential of recommendation systems. We refer to this task as Time Series Prediction Empowering Recommendations (TSPER). However, as a subtask within the recommendation system, TSPER faces unique challenges such as computational and data constraints, system evolution, and the need for performance and interpretability. To meet these unique needs, we propose a lightweight Multi-Layer Perceptron architecture with joint Time-Frequency information, named Light All-MLP with joint TimE-frEquency information (LAMEE). LAMEE utilizes a lightweight MLP architecture to achieve computing efficiency and adaptive online learning. Moreover, various strategies have been employed to improve the model, ensuring stable performance and model interpretability. Across multiple time series datasets potentially related to recommendation systems, LAMEE balances performance, efficiency, and interpretability, overall surpassing existing complex methods.