Dialogue Generation for Conversational Recommendation Using User Search Interactions and Knowledge Graphs
We propose SearCl, which automatically generates conversational recommendation dialogues grounded in user search and click behaviors.
ProofPaper ↗
Key points
- Large-scale conversational recommendation datasets typically require manual dialogue construction or annotation.
- 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.
- Using SearCl, we generate 3,538 dialogues from content search and click interaction data, covering 1,731 unique target items.
Sources (1)
- [1]Dialogue Generation for Conversational Recommendation Using User Search Interactions and Knowledge GraphsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 11:11 AM
We propose SearCl, which automatically generates conversational recommendation dialogues grounded in user search and click behaviors.
Large-scale conversational recommendation datasets typically require manual dialogue construction or annotation.
Extractive summary: sentences quoted from the sources.
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