Adapting ranking SVM to document retrieval Export

@inproceedings{citeulike:4221893, abstract = {The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a "learning to rank" method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the "Hinge Loss" function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets.}, address = {New York, NY, USA}, author = {Cao, Yunbo and Xu, Jun and Liu, Tie Y. and Li, Hang and Huang, Yalou and Hon, Hsiao W.}, booktitle = {SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, citeulike-article-id = {4221893}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1148205}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1148170.1148205}, doi = {10.1145/1148170.1148205}, isbn = {1-59593-369-7}, keywords = {learning2rank, rankingsvm, svm}, location = {Seattle, Washington, USA}, pages = {186--193}, posted-at = {2009-05-20 17:48:58}, priority = {2}, publisher = {ACM}, title = {Adapting ranking SVM to document retrieval}, url = {http://dx.doi.org/10.1145/1148170.1148205}, year = {2006} }

TY - CONF ID - citeulike:4221893 L3 - citeulike-article-id:4221893 N2 - The paper is concerned with applying learning to rank to document retrieval. Ranking SVM is a typical method of learning to rank. We point out that there are two factors one must consider when applying Ranking SVM, in general a "learning to rank" method, to document retrieval. First, correctly ranking documents on the top of the result list is crucial for an Information Retrieval system. One must conduct training in a way that such ranked results are accurate. Second, the number of relevant documents can vary from query to query. One must avoid training a model biased toward queries with a large number of relevant documents. Previously, when existing methods that include Ranking SVM were applied to document retrieval, none of the two factors was taken into consideration. We show it is possible to make modifications in conventional Ranking SVM, so it can be better used for document retrieval. Specifically, we modify the "Hinge Loss" function in Ranking SVM to deal with the problems described above. We employ two methods to conduct optimization on the loss function: gradient descent and quadratic programming. Experimental results show that our method, referred to as Ranking SVM for IR, can outperform the conventional Ranking SVM and other existing methods for document retrieval on two datasets. CY - Seattle, Washington, USA SN - 1-59593-369-7 AD - New York, NY, USA EP - 193 TI - Adapting ranking SVM to document retrieval PB - ACM T2 - SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval SP - 186 KW - learning2rank KW - rankingsvm KW - svm AU - Cao, Yunbo AU - Xu, Jun AU - Liu, Tie AU - Li, Hang AU - Huang, Yalou AU - Hon, Hsiao PY - 2006/// UR - http://dx.doi.org/10.1145/1148170.1148205 ER -