  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
