Bundle generation aims to provide a bundle of items for the user, and has been widely studied and applied on online service platforms. Existing bundle generation methods mainly utilized user’s preference from historical interactions in common recommendation paradigm, and ignored the potential textual query which is user’s current explicit intention. There can be a scenario in which a user proactively queries a bundle with some natural language description, the system should be able to generate a bundle that exactly matches the user’s intention through the user’s query and preferences. In this work, we define this user-friendly scenario as Query-based Bundle Generation task and propose a novel framework Text2Bundle that leverages both the user’s short-term interests from the query and the user’s long-term preferences from the historical interactions. Our framework consists of three modules: (1) an intention extractor based on Large Language Model that mines the user’s fine-grained interests from the query; (2) a unified state encoder that learns the current bundle context state and the user’s preferences based on historical interaction and current query; and (3) a bundle generator that generates personalized and complementary bundles using reinforcement learning with specifically designed rewards. We conduct extensive experiments on three real-world datasets and demonstrate the effectiveness of our framework compared with several state-of-the-art methods.

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.