Conversational recommender systems (CRS) aim to recommend high-quality items
to users through interactive conversations. Although several efforts have been
made for CRS, two major issues still remain to be solved. First, the
conversation data itself lacks of sufficient contextual information for
accurately understanding users' preference. Second, there is a semantic gap
between natural language expression and item-level user preference. To address
these issues, we incorporate both word-oriented and entity-oriented knowledge
graphs (KG) to enhance the data representations in CRSs, and adopt Mutual
Information Maximization to align the word-level and entity-level semantic
spaces. Based on the aligned semantic representations, we further develop a
KG-enhanced recommender component for making accurate recommendations, and a
KG-enhanced dialog component that can generate informative keywords or entities
in the response text. Extensive experiments have demonstrated the effectiveness
of our approach in yielding better performance on both recommendation and
conversation tasks.

本论文介绍了一种使用知识图谱和互信息最大化技术的对话式推荐系统，通过增强对话数据表示来解决上下文信息不足以及自然语言表达和用户偏好之间的语义鸿沟问题，从而在推荐和对话任务方面提高了性能。

基于知识图谱的语义融合改进对话式推荐系统

Improving Conversational Recommender Systems via Knowledge Graph based  Semantic Fusion

Neural generative models have become popular and achieved promising
performance on short-text conversation tasks. They are generally trained to
build a 1-to-1 mapping from the input post to its output response. However, a
given post is often associated with multiple replies simultaneously in real
applications. Previous research on this task mainly focuses on improving the
relevance and informativeness of the top one generated response for each post.
Very few works study generating multiple accurate and diverse responses for the
same post. In this paper, we propose a novel response generation model, which
considers a set of responses jointly and generates multiple diverse responses
simultaneously. A reinforcement learning algorithm is designed to solve our
model. Experiments on two short-text conversation tasks validate that the
multiple responses generated by our model obtain higher quality and larger
diversity compared with various state-of-the-art generative models.

该研究提出了一种新型的响应生成模型，使用一种强化学习算法以考虑一个响应集合，同时生成多个不同的响应，实验证明相比各种最先进的生成模型，我们模型生成的多个响应具有更高的质量和更大的多样性。