Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation

Understand

Recommender systems are ubiquitous yet often difficult for users to control, and adjust if recommendation quality is poor.

• This has motivated conversational recommender systems (CRSs), with control provided through natural language feedback.
• However, as with most application domains, building robust CRSs requires training data that reflects system usage$\unicode{x2014}$here conversations with user utterances paired with items that cover a wide range of preferences.
• This has proved challenging to collect scalably using conventional methods.