Summary of News Recommendation with Category Description by a Large Language Model, By Yuki Yada and Hayato Yamana
News Recommendation with Category Description by a Large Language Model
by Yuki Yada, Hayato Yamana
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 method for personalized news recommendations is proposed in this paper, which utilizes a large language model (LLM) to generate informative category descriptions. The goal is to enhance the categories’ descriptions and incorporate them into recommendation models as additional information. The approach achieves a 5.8% improvement at most in AUC compared with baseline approaches without the LLM’s generated category descriptions for state-of-the-art content-based recommendation models like NAML, NRMS, and NPA. Experimental evaluations using the MIND dataset validate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make personalized news recommendations more accurate. It uses a special kind of AI model to create better descriptions for different types of news articles. This helps computers understand what each article is about and recommend similar ones to users. The results show that this approach works well and can even improve the accuracy of popular recommendation algorithms. |
Keywords
» Artificial intelligence » Auc » Large language model
