Summary of Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations, by Xiaonan Xu et al.
Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
by Xiaonan Xu, Yichao Wu, Penghao Liang, Yuhang He, Han Wang
First submitted to arxiv on: 5 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 The abstract presents an overview of recommender systems, highlighting their importance in e-commerce and web applications. It notes that while deep neural networks (DNNs) have improved recommendation systems by incorporating user and item information, they still struggle to understand users’ interests and capture textual data effectively. The emergence of large language models (LLMs), such as ChatGPT and GPT-4, has revolutionized natural language processing and AI due to their superior capabilities in language understanding and generation. Recent research seeks to harness the power of LLMs to improve recommendation systems. A systematic review is needed to provide insight into existing LLM-driven recommendation systems for researchers and practitioners. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recommender systems are important parts of our daily lives, suggesting what we might like based on our preferences. While deep learning has improved these systems, they still struggle to understand what we want. This is changing with the rise of large language models (LLMs). These models can understand and generate text better than ever before. Researchers are now trying to use LLMs to make recommendation systems even better. |
Keywords
» Artificial intelligence » Deep learning » Gpt » Language understanding » Natural language processing
