DUET aligns raw user and item data by transforming them into textual profiles within a shared semantic space. Traditional recommendation systems use hidden vectors, but we advocate a shift from vectors to text: representing users and items as textual profiles that are directly compatible with LLMs and offer interpretability for downstream agentic systems.
Traditional recommendation systems represent users and items as hidden vectors, learning to align them in a shared latent space for relevance estimation. With the advent of large language models (LLMs), we advocate a shift from vectors to text: representing users and items as textual profiles and aligning them in a shared semantic space. Textual profiles are directly compatible with LLMs and offer interpretability for downstream agentic systems.
A key challenge, however, is that the optimal profile format is unknown, and handcrafted templates often misalign with task objectives. We propose DUET, a framework for joint exploration of user–item profile generation in text. The framework operates in three stages: First, raw histories and metadata are distilled into simple cues that capture minimal but informative signals. Second, during a single sequence-to-sequence inference pass, these cues are expanded into richer prompts and then into textual profiles, allowing for the exploration of multiple formats rather than a single creation.
Finally, profiles are optimized jointly via reinforcement learning, where downstream recommendation performance provides feedback to refine and align them. Experiments on three real-world datasets demonstrate that DUET outperforms strong baselines, validating the effectiveness of joint textual profile alignment and the utility of prompt-driven exploration.
