StyleCSR: Style-controllable Sequential Recommendation with Prompt Tuning

Leilei Wen, Qi Shen, Shixuan Zhu, Zhihua Wei, Wen Shen, Lijun Sun

Abstract: Controllability is a key factor in building user-trustworthy recommendation systems. However, most existing studies lack consideration for controllability, relying solely on users’ historical interaction data to continuously recommend items that align with their past preferences. This approach may isolate users from the outside world, leading to the "filter bubble" phenomenon. As a result, users may still feel dissatisfied, even when the recommendation accuracy is high. In this work, we propose a user-friendly, style-controllable sequential recommendation framework with prompt tuning (StyleCSR). This framework enables users to provide instructions for adjusting item features, such as popularity and similarity, to control the distribution of recommendation results. Specifically, we map user instructions into prompts and fuse them with user historical behavior sequences. To enhance the controllability of recommendation results while maintaining high recommendation performance, we design an interest alignment module to retain users’ original interests and an instruction discrimination module to emphasize the role of user instructions. We conduct extensive experiments on multiple datasets and various types of pre-trained sequential recommendation models. The results validate that our StyleCSR can be applied to different types of pre-trained models, significantly enhancing the controllability of recommendation results while maintaining high recommendation performance.

External IDs:dblp:conf/ijcnn/WenSZWSS25