抄録
Dialogue contains a wealth of information about speakers’ preferences and experiences, which can be leveraged to personalize and suggest advanced information in various systems. The task of a conversational recommender system is to make recommendations through dialogue. However, existing approaches do not adequately consider the preference information obtained from the dialogue. Moreover, dialogue-based recommendations face unique challenges, such as noise during dialogue and a lack of detailed item information. We introduce the SumRec framework for dialogue-based recommendations, which utilizes information from speaker summaries and recommendation sentences. In this framework, a large language model (LLM) generates summaries focusing on the speaker and sentences recommending items, thereby extracting features of both the speaker and the item. A speaker summary condenses the dialogue to highlight the speaker’s interests, preferences, and experiences. Recommendation sentences describe the type of users who would prefer the item, facilitating an appropriate link between the speaker and the item information. The score estimator then uses this information to predict how likely the speaker is to appreciate the item. To train and evaluate SumRec, we developed ChatRec, a dataset for recommending tourist attractions based on chat dialogues between two individuals. This dataset includes information on tourist destinations, their rating scores by speakers, and predicted scores by third parties. Experimental results using ChatRec showed that SumRec outperformed the baseline method, which relied solely on dialogue and item information. Further experiments with REDIAL, an existing recommendation dialogue dataset, demonstrated similar performance improvements with SumRec.