Refining Text Generation for Realistic Conversational Recommendation via Direct Preference Optimization (2025.emnlp-main)
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| Challenge: | Existing Conversational Recommender Systems (CRSs) deviate from real human interactions by rapidly recommending items in brief sessions. |
| Approach: | They propose to use Large Language Models to generate dialogue summaries from dialogue history and item recommendation information from item description to extract both explicit user statements and implicit preferences inferred from the dialogue context. |
| Outcome: | The proposed method extracts both explicit user statements and implicit preferences inferred from the dialogue context. |
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