Recommendation Dialogue

Recommendation dialogue systems aim to improve the recommendation process by engaging users in natural language conversations, thereby personalizing suggestions and enhancing user satisfaction. Current research focuses on developing models that effectively incorporate user preferences and internal states (e.g., knowledge level, interest) into the recommendation process, often leveraging large language models (LLMs) and asynchronous frameworks for efficient response generation. These advancements are driven by the need for high-quality training data, leading to efforts in automatic dataset generation and improved data annotation techniques to enhance model performance and explainability. The resulting improvements in recommendation accuracy and user experience have significant implications for various applications, including e-commerce, entertainment, and personalized information services.