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| listopt: learning to optimize for xml ranking | |
| Gao Ning; Deng Zhi-Hong; Yu Hang; Jiang Jia-Jian | |
| 2011 | |
| Conference Name | 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 |
| Source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 482-492 |
| Conference Date | 24-May-20 |
| Conference Place | Shenzhen, China |
| Indexed Type | ei |
| Publish Place | Germany |
| ISSN | 3029743 |
| ISBN | 9783642208461 |
| Department | (1) Key Laboratory of Machine Perception (Ministry of Education), School of Electronic Engineering and Computer Science, Peking University, China; (2) State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China |
| English Abstract | Many machine learning classification technologies such as boosting, support vector machine or neural networks have been applied to the ranking problem in information retrieval. However, since the purpose of these learning-to-rank methods is to directly acquire the sorted results based on the features of documents, they are unable to combine and utilize the existing ranking methods proven to be effective such as BM25 and PageRank. To solve this defect, we conducted a study on learning-to-optimize, which is to construct a learning model or method for optimizing the free parameters in ranking functions. This paper proposes a listwise learning-to-optimize process ListOPT and introduces three alternative differentiable query-level loss functions. The experimental results on the XML dataset of Wikipedia English show that these approaches can be successfully applied to tuning the parameters used in an existing highly cited ranking function BM25. Furthermore, we found that the formulas with optimized parameters indeed improve the effectiveness compared with the original ones. © 2011 Springer-Verlag. |
| Keyword | Adaptive Boosting Data Mining Information Retrieval Neural Networks Xml |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/14269 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Gao Ning,Deng Zhi-Hong,Yu Hang,et al. listopt: learning to optimize for xml ranking[C]. Germany,2011:482-492. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| listopt learning to（228KB） | 开放获取 | -- | Application Full Text | |||
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