Summary of Probably Approximately Precision and Recall Learning, by Lee Cohen et al.
Probably Approximately Precision and Recall Learning
by Lee Cohen, Yishay Mansour, Shay Moran, Han Shao
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel approach to precision and recall maximization is proposed in this paper, which tackles the challenge of one-sided feedback prevalent in recommender systems and multi-label learning tasks. The authors introduce a new method that balances precision (proportion of relevant items among predicted ones) and recall (proportion of relevant items successfully predicted) by incorporating negative feedback from test data. This approach is particularly useful in scenarios where only positive examples are available during training, such as YouTube’s recommender system. The proposed method is evaluated on various benchmarks, demonstrating improved performance compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in machine learning, where you need both good predictions and complete coverage. Think of it like finding all the best movies on Netflix – you want to get most of your favorite shows, but not miss out on great ones. The authors show how to make models that balance being accurate (precision) with covering many possibilities (recall). They do this by using feedback from things that don’t happen, like a user skipping a movie. |
Keywords
» Artificial intelligence » Machine learning » Precision » Recall
