Summary of Multi-margin Cosine Loss: Proposal and Application in Recommender Systems, by Makbule Gulcin Ozsoy
Multi-Margin Cosine Loss: Proposal and Application in Recommender Systems
by Makbule Gulcin Ozsoy
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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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 The proposed Multi-Margin Cosine Loss (MMCL) is a novel approach for improving the performance of recommender systems. By introducing multiple margins and varying weights for negative samples, MMCL efficiently utilizes both the hardest negatives and other non-trivial negatives. This results in a simpler yet effective loss function that outperforms more complex methods, especially when resources are limited. The proposed method is evaluated on two well-known datasets, demonstrating a 20% performance improvement compared to a baseline loss function when using fewer negative samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recommender systems help people find the things they like by suggesting items based on what they liked before. Researchers have been trying to make these systems better and more efficient. One way is by improving how they learn from user feedback, like ratings or clicks. A new approach called Multi-Margin Cosine Loss (MMCL) is designed to do just that. MMCL uses different weights for different negative samples, making it more effective with limited resources. |
Keywords
» Artificial intelligence » Loss function
