Make your next item recommendation model time sensitive

Abstract

Recommender systems have become increasingly popular for providing personalized recommendations to users. Recent studies have shown that transformer-based approaches can enhance the performance of these systems. However, these models usually consider the sequence of past user interactions and do not take into account the time of prediction. In this paper, we address this issue by proposing a simple yet effective method in the form of adapter to make next-item recommenders time-aware. Specifically, we introduce a novel approach that incorporates time information into the modeling process. We conduct extensive experiments on two commonly used sequential recommenders, GRU4Rec and TiSASRec, using four real-world datasets. Our results demonstrate that our approach increases the quality of existing methods and improves the accuracy of recommendations. Our approach is easy to implement and can be applied to a wide range of next-item recommendation systems. It provides a structured framework for incorporating time information into the modeling process, making it easier for researchers to replicate and build upon our findings. Overall, our work contributes to the development of more accurate and efficient recommendation systems, with potential applications in various domains such as e-commerce, social media, and online content delivery. Code is available at GitHub repo1.