Summary of Re2llm: Reflective Reinforcement Large Language Model For Session-based Recommendation, by Ziyan Wang et al.
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
by Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua, Kaidong Feng, Wenya Wang, Jie Zhang
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel approach to enhance session-based recommendation (SBR) is proposed, leveraging Large Language Models (LLMs). The existing prompt-based and fine-tuning-based methods have limitations, such as suboptimal prompts and high computational costs. To address these issues, the Reflective Reinforcement Large Language Model (Re2LLM) is introduced, guiding LLMs to focus on specialized knowledge essential for accurate recommendations. The Re2LLM consists of two modules: Reflective Exploration Module, which extracts understandable knowledge, and Reinforcement Utilization Module, training a lightweight retrieval agent to select hints from the constructed knowledge base. This method consistently outperforms state-of-the-art methods across multiple real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers give better suggestions is being developed. Large Language Models (LLMs) are very good at giving suggestions, but they can be improved. The current ways of improving LLMs have some problems, like not always knowing what to ask them and taking a lot of computer power. To solve these problems, a new model called Re2LLM is being created. It helps the LLM focus on the right information by looking at mistakes it makes and using that to learn from. This new model does better than other models in tests. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge base » Large language model » Prompt
