Summary of Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks, By Jaehyun Lee et al.
Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks
by Jaehyun Lee, SeongKu Kang, Hwanjo Yu
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 new approach is proposed to improve graph neural network (GNN)-based matrix completion by exploiting the ordinal nature of ratings. The method, called ROGMC, incorporates cumulative preference propagation and interest regularization to emphasize users’ stronger preferences based on rating types. This outperforms existing strategies in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recommender systems can predict what you’ll like based on what others have liked before. A new way to make this better is by looking at how much people like something, not just if they like it or not. This helps the system understand that someone who loves a movie really loves it, and someone who likes a book okay might like a similar book even more. The new approach uses a special kind of neural network called a graph neural network to make these predictions, and it works better than other methods. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Neural network » Regularization
