Dialogue Generation for Conversational Recommendation Using User Search Interactions and Knowledge Graphs
Large-scale conversational recommendation datasets typically require manual dialogue construction or annotation. We propose SearCl, which automatically generates conversational recommendation dialogues grounded in user search and click behaviors. SearCl segments user interactions into query sessions and derives each recommendation target from content selected after a search query. A configurable probabilistic state machine controls the dialogue flow, while two LLM agents acting as the seeker and recommender generate utterances according to turn-level constraints. To support recommendation, the recommender is provided with knowledge graphs constructed from metadata about the content items ...
| Authors | Minsung Hwang, Donggyu Yoon, Gowoon Hwang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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