Linear Sequential Recommendation Models using Textual Side Information

Dongcheol Lee, Minjin Choi, Jongwuk Lee

http://doi.org/10.5626/JOK.2025.52.6.529

Recently, research on leveraging auxiliary information in sequential recommendation systems is being actively conducted. Most approaches have focused on combining language models with deep neural networks. However, they often lead to high computational costs and latency issues. While linear recommendation models can serve as an efficient alternative, research on how to effectively incorporate auxiliary information is lacking. This study proposed a framework that could effectively utilize auxiliary information within a linear model. Since textual data cannot be directly used in linear model training, we transformed item texts into dense vectors using a pre-trained text encoder. Although these vectors contained rich information, they failed to capture relationships between items. To address this, we applied graph convolution to obtain enhanced item representations. These representations were then used alongside the user-item interaction matrix for linear model training. Extensive experiments showed that the proposed method improved the overall performance, particularly in recommending less popular items.