Summary of Personalized News Recommendation System Via Llm Embedding and Co-occurrence Patterns, by Zheng Li and Kai Zhange
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns
by Zheng Li, Kai Zhange
First submitted to arxiv on: 9 Nov 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 authors propose a novel news recommendation algorithm called LECOP (LLM Embedding and Co-Occurrence Pattern) that leverages large language models (LLMs) for personalized news suggestions. The LECOP approach combines two key components: fintuned LLMs that encode news using contrastive learning, and multiple co-occurrence patterns that capture collaborative information between news articles. These patterns include news ID co-occurrence, item-item keywords co-occurrence, and intra-item keywords co-occurrence, all generated by LLMs. Experimental results demonstrate the superior performance of the proposed method in terms of recommendation accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to recommend news articles that uses large language models (LLMs) to understand what people like to read. The authors combine two ideas: using LLMs to understand each news article, and looking at how different articles are related to each other. They call this approach LECOP, which stands for Large Language Model Embedding and Co-Occurrence Pattern. This method is the first of its kind and shows great promise in recommending personalized news. |
Keywords
» Artificial intelligence » Embedding » Large language model
