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Music recommendation for videos attracts growing interest in multi-modalresearch. However, existing systems focus primarily on content compatibility,often ignoring the users' preferences. Their inability to interact with usersfor further refinements or to provide explanations leads to a less satisfyingexperience. We address these issues with MuseChat, a first-of-its-kinddialogue-based recommendation system that personalizes music suggestions forvideos. Our system consists of two key functionalities with associated modules:recommendation and reasoning. The recommendation module takes a video alongwith optional information including previous suggested music and user'spreference as inputs and retrieves an appropriate music matching the context.The reasoning module, equipped with the power of Large Language Model(Vicuna-7B) and extended to multi-modal inputs, is able to provide reasonableexplanation for the recommended music. To evaluate the effectiveness ofMuseChat, we build a large-scale dataset, conversational music recommendationfor videos, that simulates a two-turn interaction between a user and arecommender based on accurate music track information. Experiment results showthat MuseChat achieves significant improvements over existing video-based musicretrieval methods as well as offers strong interpretability andinteractability.

MuseChat: A Conversational Music Recommendation System for Videos