The field of conversational recommender systems is moving towards more sophisticated and effective methods of incorporating contextual information and generating personalized recommendations. Researchers are exploring new approaches to combine different types of contextual information, such as structured and unstructured data, to improve the accuracy of recommendations. Additionally, there is a growing interest in using generative models and causal inference to create more adaptive and persuasive conversational systems. Another trend is the development of methods to overcome the limitations of traditional conversational recommender systems, such as the need for extensive domain-specific datasets, by leveraging large language models and active data augmentation techniques. Noteworthy papers in this area include:

• One that proposes a multi-type context-aware conversational recommender system, effectively fusing multi-type contextual information via mixture-of-experts.
• Another that enhances the process of counterfactual inference through causal discovery, identifying strategy-level causal relationships among user and system utterances.
• A paper that proposes an active data augmentation framework, synthesizing conversational training data by leveraging black-box LLMs guided by active learning techniques.
• A novel model that introduces contextual disentanglement for improving conversational recommender systems, employing a dual disentanglement framework to effectively distinguish focus information and background information from the dialogue context.