In recent years, great efforts have been made to develop a conversational recommender system (CRS). However, existing works always ignore the incorporation of the recommended items and the generated replies. This causes the performance of the recommendation to degrade in the conversations. To solve this problem, we propose a novel framework called unified conversational recommender system based on item-guided conditional generation (UCRI) to fuse the recommender module and the dialogue module seamlessly. UCRI captures the semantic similarity between the recommended items and the candidate words to realize the item-guided conditional generation. Besides, we further design the weight control mechanism and the recommender gating mechanism to make accurate recommendations in the conversations. Our approach can explicitly generate the recommended items in the replies and encourage the model to generate the related context for the items. Extensive experiments on the benchmark dataset REcommendations through DIALog show that our model achieves the best performance on both item recommendation and reply generation tasks.

中文翻译：

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UCRI：基于项目引导条件生成的统一会话推荐系统

近年来，人们在开发对话式推荐系统（CRS）方面做出了巨大的努力。然而，现有的工作总是忽略推荐项目和生成的回复的结合。这会导致对话中推荐的性能下降。为了解决这个问题，我们提出了一种基于项目引导条件生成（UCRI）的统一会话推荐系统的新颖框架，以无缝融合推荐模块和对话模块。UCRI捕获推荐项目和候选词之间的语义相似性，以实现项目引导的条件生成。此外，我们还进一步设计了权重控制机制和推荐门控机制，以在对话中做出准确的推荐。我们的方法可以在回复中明确生成推荐的项目，并鼓励模型生成项目的相关上下文。通过 DIALog 对基准数据集 REcommendations 进行的大量实验表明，我们的模型在项目推荐和回复生成任务上均实现了最佳性能。