Building large-scale conversational recommendation datasets is a bottleneck for training systems, since dialogues are usually written or annotated by humans. A new arXiv preprint proposes SearCl, a method that automatically generates such dialogues from user search interactions and knowledge graphs.
The idea is to ground generated conversations in real user behavior and structured knowledge, rather than relying on handcrafted scripts. This could make it easier to produce diverse, realistic training data at scale, without the cost of manual annotation.
The abstract is brief and does not yet report experimental results, so the practical effectiveness of SearCl remains to be seen. Still, the approach points toward a more automated pipeline for conversational recommendation research.