Summary of Psl: Rethinking and Improving Softmax Loss From Pairwise Perspective For Recommendation, by Weiqin Yang et al.
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
by Weiqin Yang, Jiawei Chen, Xin Xin, Sheng Zhou, Binbin Hu, Yan Feng, Chun Chen, Can Wang
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper analyzes the Softmax Loss (SL) function widely used in recommender systems and identifies two limitations: its loose relationship with conventional ranking metrics like DCG, and high sensitivity to false negative instances. By examining the use of the exponential function in SL, the authors reveal that these issues can be addressed by extending SL to a new family of loss functions, termed Pairwise Softmax Loss (PSL). PSL replaces the exponential function with alternative activation functions, offering three benefits: serving as a tighter surrogate for DCG, balancing data contributions better, and enhancing BPR loss through Distributionally Robust Optimization. Empirical experiments validate the effectiveness and robustness of PSL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make recommender systems better by changing the way they calculate “loss” (a measure of how good or bad a system is). The current way, called Softmax Loss (SL), has some problems: it doesn’t match up well with other ways of measuring ranking quality, and it’s easily fooled by incorrect answers. To fix these issues, the authors create new types of loss functions that replace part of SL. These new functions have three good qualities: they’re a better fit for matching rankings, they make sure all data is used fairly, and they improve on an existing method called BPR. The paper shows how well these new loss functions work in real-world experiments. |
Keywords
* Artificial intelligence * Optimization * Softmax
