Summary of Uniting Contrastive and Generative Learning For Event Sequences Models, by Aleksandr Yugay and Alexey Zaytsev
Uniting contrastive and generative learning for event sequences models
by Aleksandr Yugay, Alexey Zaytsev
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper proposes a novel approach to represent transactional sequences in modern banking applications, such as risk management and churn prediction. The authors highlight the importance of capturing both local and global representation properties, which are essential for different tasks. They review previous research on self-supervised approaches, which have shown promise in either capturing local or global qualities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to represent customer transactions in banking that helps with things like predicting when customers might leave (churn prediction) and offering them personalized deals. Different tasks require different types of representations: some need to capture what’s happening right now, while others need to understand general patterns. The authors look at previous research on self-supervised methods and how they can be used for this problem. |
Keywords
* Artificial intelligence * Self supervised
