Summary of Sumrec: a Framework For Recommendation Using Open-domain Dialogue, by Ryutaro Asahara et al.
SumRec: A Framework for Recommendation using Open-Domain Dialogue
by Ryutaro Asahara, Masaki Takahashi, Chiho Iwahashi, Michimasa Inaba
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework called SumRec for recommending information from open-domain chat dialogues. The authors use a large language model (LLM) to generate a summary of the speaker’s information and recommend items based on their characteristics. The framework is evaluated using ChatRec, a newly constructed dataset for training and evaluation. The results show that SumRec provides better recommendations than the baseline method, demonstrating its effectiveness in personalizing systems and offering recommendations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to use chat dialogues to get personalized information. Imagine talking to someone who knows what you like and can recommend things just for you! The researchers created a special system called SumRec that takes chat conversations and uses them to give recommendations. They tested this system with a new dataset they made, called ChatRec, and found it worked better than just using the original conversation text. This could be useful for systems like search engines or recommendation platforms. |
Keywords
* Artificial intelligence * Large language model
