Generative Conversational Recommender System

• URL: http://arxiv.org/abs/2605.21987v1
• Date: Thu, 21 May 2026 04:36:37 GMT
• Title: Generative Conversational Recommender System
• Abstract summary: We propose a fully generative conversational recommender system that unifies recommendation and dialog generation within a single autoregressive framework.<n>Our method consistently improves recommendation performance, achieving gains of up to 29% on Recall@1 over strong baselines.
• Score: 32.13241752028528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conversational recommender systems aim to provide personalized recommendations via natural language interactions. However, existing approaches either decouple recommendation from dialog generation or rely on retrieval-based pipelines, limiting the integration between recommendation and response generation and leading to suboptimal modeling of user intent. In this paper, we propose a fully generative conversational recommender system that unifies recommendation and dialog generation within a single autoregressive framework. Our approach represents items as discrete semantic IDs and integrates them directly into the generation process, enabling joint prediction of items and responses via next-token modeling. We further introduce a structured generation paradigm that factorizes conversational recommendation into a sequence of interdependent decisions, where the model first predicts the response intent and the recommendation target, and then generates the response conditioned on them. This design enables end-to-end optimization, enforces a more coherent dependency structure, and supports faithful item generation via constrained decoding. Extensive experiments demonstrate that our method consistently improves recommendation performance, achieving gains of up to 29% on Recall@1 over strong baselines, while maintaining competitive dialog quality.

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