SumRec: A Framework for Recommendation using Open-Domain Dialogue

Chat dialogues contain considerable useful information about a speaker's interests, preferences, and experiences.Thus, knowledge from open-domain chat dialogue can be used to personalize various systems and offer recommendations for advanced information.This study proposed a novel framework SumRec for recommending information from open-domain chat dialogue.The study also examined the framework using ChatRec, a newly constructed dataset for training and evaluation. To extract the speaker and item characteristics, the SumRec framework employs a large language model (LLM) to generate a summary of the speaker information from a dialogue and to recommend information about an item according to the type of user.The speaker and item information are then input into a score estimation model, generating a recommendation score.Experimental results show that the SumRec framework provides better recommendations than the baseline method of using dialogues and item descriptions in their original form. Our dataset and code is publicly available at https://github.com/Ryutaro-A/SumRec

Enhancing Consistency in Multimodal Dialogue System Using LLM with Dialogue Scenario

This paper describes our dialogue system submitted to Dialogue Robot Competition 2023. The system's task is to help a user at a travel agency decide on a plan for visiting two sightseeing spots in Kyoto City that satisfy the user. Our dialogue system is flexible and stable and responds to user requirements by controlling dialogue flow according to dialogue scenarios. We also improved user satisfaction by introducing motion and speech control based on system utterances and user situations. In the preliminary round, our system was ranked fifth in the impression evaluation and sixth in the plan evaluation among all 12 teams.