Abstract:A ranking model utilizing the multiple hyperplanes optimized by the order relations is proposed based on RankSVM in this paper. Firstly, the multiple hyperplanes are built based on the order relations between the ranks for training data in this model. Then, the ranking list generated by multiple hyperplanes is aggregated to gain the final ranking results. The proposed model is tested on LETOR OHSUMED dataset, some typical indices in Information Retrieval field being applied to evaluate its performance and the method being compared with other methods such as RankSVM. The experimental results show that the model not only has better ranking performance but also shorten the training time evidently.
孙鹤立,冯博琴,黄健斌,赵英良,刘均. 序关系优化的多超平面排序学习模型[J]. 模式识别与人工智能, 2010, 23(3): 327-334.
SUN He-Li,FENG Bo-Qin,HUANG Jian-Bin,ZHAO Ying-Liang,LIU Jun. Ranking Model of Optimized Multiple Hyperplanes Using Order Relations. , 2010, 23(3): 327-334.
[1] Robertson S, Hull D A. The TREC-9 Filtering Track Final Report // Proc of the 8th Text Retrieval Conference. Gaithersburg, USA, 2000: 25-40 [2] Baeza-Yates R, Ribeiro-Neto B. Modern Information Retrieval. Upper Saddle River, USA: Addison Wesley, 1999 [3] Joachims T. Optimizing Search Engines Using Click through Data // Proc of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Canada, 2002: 133-142 [4] Burges C, Shaked T, Renshaw E, et al. Learning to Rank Using Gradient Descent // Proc of the 22nd International Conference on Machine Learning. Bonn, Germany, 2005: 89-96 [5] Freund Y, Iyer R D, Schapire R E, et al. An Efficient Boosting Algorithm for Combining Preferences. The Journal of Machine Learning Research, 2003, 4: 933-969 [6] Cao Yunbo, Xu Jun, Liu Tieyan, et al. Adapting Ranking SVM to Document Retrieval // Proc of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, USA, 2006: 186-193 [7] Qin Tao, Zhang Xudong, Wang Desheng, et al. Ranking with Multiple Hyperplanes // Proc of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, Netherlands, 2007: 279-286 [8] Xu Jun, Li Hong. AdaRank: A Boosting Algorithm for Information Retrieval // Proc of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, Netherlands, 2007: 391-398 [9] Cao Zhe, Qin Tao, Liu Tieyan, et al. Learning to Rank: From Pairwise Approach to Listwise Approach // Proc of the 24th International Conference on Machine Learning. Corvalis, USA, 2007: 129-136 [10] Qin Tao, Zhang Xudong, Tsai M F, et al. Query-Level Loss Functions for Information Retrieval. Information Processing and Management: An International Journal, 2008, 44(2): 838-855 [11] Burges C, Ragno R, Le Q. Learning to Rank with Nonsmooth Cost functions // Proc of the 20th Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2006: 395-402 [12] Tsai M F, Liu Tieyan, Qin Tao, et al. Frank: A Ranking Method with Fidelity Loss // Proc of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, Netherlands, 2007: 383-390 [13] Qin Tao, Liu Tieyan, Zhang Xudong, et al. Global Ranking Using Continuous Conditional Random Fields // Proc of the 22nd Annual Conference on Neural Information Processing Systems. Vancouver, Canada, 2008: 1281-1288 [14] Qin Tao, Liu Tieyan, Zhang Xudong, et al. Learning to Rank Relational Objects and Its Application to Web Search // Proc of the 17th International Conference on World Wide Web. Beijing, China, 2008: 407-416 [15] Chakrabarti S, Khanna R, Sawant U, et al. Structured Learning for Non-Smooth Ranking Losses // Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Los Vegas, USA, 2008: 88-96 [16] Yue Yisong, Finley T, Radlinski F, et al. A Support Vector Method for Optimizing Average Precision // Proc of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, Netherlands, 2007: 271-278 [17] Xu Jun, Liu Tieyan, Lu Min, et al. Directly Optimizing Evaluation Measures in Learning to Rank // Proc of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Singapore, Singapore, 2008: 107-114 [18] Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167 [19] Jrvelin K, Keklinen J. IR Evaluation Methods for Retrieving Highly Relevant Documents // Proc of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Athers, Greece, 2000: 41-48 [20] Hersh W R, Buckley C, Leone T J, et al. OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research // Proc of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 1994: 192-201 [21] Chang C C, Lin C J. LIBSVM: A Library for Support Vector Machines [DB/OL]. [2001-04-03]. http://www.csie.ntu.edu.tw/~cjlin/libsvm