[3E1-GS-10] AI application:

[3E1-GS-10-04] Recommendation List Generation Considering Interactions Between Items Using a Transformer

〇Ryunosuke Ikeda¹, Kengo Hamada¹, Ryo Takahashi¹ (1. Recruit Co., Ltd.)

Keywords:Recommendation, Transformer

In this study, we propose a recommendation list generation method that considers the mutual influence between items displayed simultaneously in a recommendation list. Traditionally, ”bundle recommendation” has been known as a method that aims to optimize the entire recommendation list while considering inter-item influence. However, few studies explicitly address the mutual influence within the list, i.e., the range that users actually see. This study tackles this issue by incorporating users’ behavior of ”comparing simultaneously presented items while making a selection” into the model, thereby improving prediction accuracy. The proposed method utilizes a Transformer- based model to predict item scores while considering the features of other items displayed simultaneously in the list. Furthermore, to address the challenge of the combinatorial explosion in optimizing the recommendation list, we propose an algorithm that employs a greedy method to generate recommendation lists within a feasible computation time. Finally, numerical experiments using open datasets are conducted to validate the effectiveness of the proposed method.