Conversational recommender system (CRS) needs to be seamlessly integrated between the two modules of recommendation and dialog, aiming to recommend high-quality items to users through multiple rounds of interactive dialogs. Items can typically refer to goods, movies, news, etc. Through this form of interactive dialog, users can express their preferences in real time, and the system can fully understand the user’s thoughts and recommend corresponding items. Although mainstream dialog recommendation systems have improved the performance to some extent, there are still some key issues, such as insufficient consideration of the entity’s order in the dialog, the different contributions of items in the dialog history, and the low diversity of generated responses. To address these shortcomings, we propose an improved dialog context model based on time-series features. Firstly, we augment the semantic representation of words and items using two external knowledge graphs and align the semantic space using mutual information maximization techniques. Secondly, we add a retrieval model to the dialog recommendation system to provide auxiliary information for generating replies. We then utilize a deep timing network to serialize the dialog content and more accurately learn the feature relationship between users and items for recommendation. In this paper, the dialog recommendation system is divided into two components, and different evaluation indicators are used to evaluate the performance of the dialog component and the recommendation component. Experimental results on widely used benchmarks show that the proposed method is effective.

中文翻译：

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改进的基于对话上下文的会话推荐系统

会话推荐系统（CRS）需要在推荐和对话两个模块之间无缝集成，旨在通过多轮交互对话向用户推荐高质量的商品。物品通常可以指商品、电影、新闻等。通过这种交互对话的形式，用户可以实时表达自己的喜好，系统可以充分了解用户的想法并推荐相应的物品。尽管主流对话推荐系统在一定程度上提高了性能，但仍然存在一些关键问题，例如对对话中实体的顺序考虑不足、对话历史中项目的不同贡献以及生成的响应多样性较低等。为了解决这些缺点，我们提出了一种基于时间序列特征的改进的对话上下文模型。首先，我们使用两个外部知识图来增强单词和项目的语义表示，并使用互信息最大化技术来对齐语义空间。其次，我们在对话推荐系统中添加检索模型，为生成回复提供辅助信息。然后，我们利用深度时序网络来序列化对话内容，并更准确地学习用户和项目之间的特征关系以进行推荐。本文将对话推荐系统分为两个组件，采用不同的评价指标来评价对话组件和推荐组件的性能。在广泛使用的基准上的实验结果表明，所提出的方法是有效的。