ITS-Rec: A Sequential Recommendation Model Using Item Textual Information

Abstract

As the e-commerce industry rapidly expands, the number of users and items continues to grow, making it increasingly difficult to capture users’ purchasing patterns. Sequential recommendation models have emerged to address this issue by predicting the next item that a user is likely to purchase based on their historical behavior. However, most previous studies have focused primarily on modeling item sequences using item IDs without leveraging rich item-level information. To address this limitation, we propose a sequential recommendation model called ITS-Rec that incorporates various types of textual item information, including item titles, descriptions, and online reviews. By integrating these components into item representations, the model captures both detailed item characteristics and signals related to purchasing motivation. ITS-Rec is built on a self-attention-based architecture that enables the model to effectively learn both the long- and short-term user preferences. Experiments were conducted using real-world Amazon.com data, and the proposed model was compared to several state-of-the-art sequential recommendation models. The results demonstrate that ITS-Rec significantly outperforms the baseline models in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Further analysis showed that online reviews contributed the most to performance gains among textual components. This study highlights the value of incorporating textual features into sequential recommendations and provides practical insights into enhancing recommendation performance through richer item representations.