| dc.description.abstract | As e-commerce thrives, the variety of online items continues to surge, with users often requiring users to invest significant time and energy in searching for items that meet their needs and preferences. In this scenario, the indispensable role of recommendation systems becomes evident, as they can deliver tailored recommendations to achieve business goals. Therefore, in this paper, we introduce an innovative recommendation model named T3CRec, designed to overcome the common inability of existing methods to effectively capture a more comprehensive context and characteristics of user-item interactions. By incorporating three contextual factors - item category, user characteristics, and item popularity - into a transformer-based framework, T3CRec can provide more diverse and personalized recommendations. We integrate item categories into item representations and use matrix factorization to map user-item interaction information into a low-dimensional space for data augmentation. Furthermore, we propose a novel way to compute item popularity, considering the average interaction interval between users and items. Through extensive experiments on real-world datasets, we demonstrated that our model outperforms the existing state-of-the-art recommendation models across all evaluation metrics, achieving an average improvement of 2.63% in recommendation performance. | en_US |