  Music recommendation for videos attracts growing interest in multi-modal
research. However, existing systems focus primarily on content compatibility,
often ignoring the users' preferences. Their inability to interact with users
for further refinements or to provide explanations leads to a less satisfying
experience. We address these issues with MuseChat, a first-of-its-kind
dialogue-based recommendation system that personalizes music suggestions for
videos. Our system consists of two key functionalities with associated modules:
recommendation and reasoning. The recommendation module takes a video along
with optional information including previous suggested music and user's
preference 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 reasonable
explanation for the recommended music. To evaluate the effectiveness of
MuseChat, we build a large-scale dataset, conversational music recommendation
for videos, that simulates a two-turn interaction between a user and a
recommender based on accurate music track information. Experiment results show
that MuseChat achieves significant improvements over existing video-based music
retrieval methods as well as offers strong interpretability and
interactability.
