Summary of A Temporal Graph Network Framework For Dynamic Recommendation, by Yejin Kim et al.
A Temporal Graph Network Framework for Dynamic Recommendation
by Yejin Kim, Youngbin Lee, Vincent Yuan, Annika Lee, Yongjae Lee
First submitted to arxiv on: 24 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 The paper proposes the application of Temporal Graph Networks (TGNs) to recommender systems, which can improve user engagement on platforms like e-commerce and streaming services by adapting to evolving preferences. By leveraging real-world datasets and various graph and history embedding methods, the study demonstrates TGN’s effectiveness in dynamic recommendation scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to use Temporal Graph Networks (TGNs) to make better recommendations for users. This is important because people’s tastes change over time, but current systems don’t always account for this. The researchers used real-life data and different methods to test TGN in recommender systems and found it works well. |
Keywords
* Artificial intelligence * Embedding
