Summary of Tsprank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model, by Weixian Waylon Li et al.
TSPRank: Bridging Pairwise and Listwise Methods with a Bilinear Travelling Salesman Model
by Weixian Waylon Li, Yftah Ziser, Yifei Xie, Shay B. Cohen, Tiejun Ma
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 authors address the limitations of traditional Learning-To-Rank (LETOR) approaches by introducing Travelling Salesman Problem Rank (TSPRank), a hybrid pairwise-listwise ranking method. TSPRank reframes the ranking problem as a Travelling Salesman Problem, leveraging combinatorial optimisation to determine listwise rankings. This approach can be integrated with existing backbone models to enhance ranking performance. Experimental results across three backbone models on diverse tasks demonstrate that TSPRank outperforms pure pairwise and listwise methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TSPRank is a new way of doing Learning-To-Rank (LETOR) that combines two old ideas, RankNet and LambdaMART, with another idea called the Travelling Salesman Problem. This helps make rankings better by looking at more than just one thing at a time. It’s like planning a trip where you have to visit lots of places in the right order. The authors tested this new method on three different things (stock ranking, information retrieval, and historical events ordering) and it worked really well. It can be used with other models to make them better too. |
