An Unsupervised Learning Algorithm for Rank Aggregation Export

@incollection{citeulike:2925401, abstract = {Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as opposed to a classification. Furthermore, for many such problems, multiple established ranking models have been well studied and it is desirable to combine their results into a joint ranking, a formalism denoted as rank aggregation. This work presents a novel unsupervised learning algorithm for rank aggregation (ULARA) which returns a linear combination of the individual ranking functions based on the principle of rewarding ordering agreement between the rankers. In addition to presenting ULARA, we demonstrate its effectiveness on a data fusion task across ad hoc retrieval systems.}, author = {Klementiev, Alexandre and Roth, Dan and Small, Kevin}, citeulike-article-id = {2925401}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-74958-5_60}, doi = {10.1007/978-3-540-74958-5_60}, journal = {Machine Learning: ECML 2007}, keywords = {rank-aggregation, ranking}, pages = {616--623}, posted-at = {2008-11-04 11:21:21}, priority = {2}, title = {An Unsupervised Learning Algorithm for Rank Aggregation}, url = {http://dx.doi.org/10.1007/978-3-540-74958-5_60}, year = {2007} }

TY - CHAP ID - citeulike:2925401 L3 - citeulike-article-id:2925401 N2 - Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as opposed to a classification. Furthermore, for many such problems, multiple established ranking models have been well studied and it is desirable to combine their results into a joint ranking, a formalism denoted as rank aggregation. This work presents a novel unsupervised learning algorithm for rank aggregation (ULARA) which returns a linear combination of the individual ranking functions based on the principle of rewarding ordering agreement between the rankers. In addition to presenting ULARA, we demonstrate its effectiveness on a data fusion task across ad hoc retrieval systems. JF - Machine Learning: ECML 2007 EP - 623 TI - An Unsupervised Learning Algorithm for Rank Aggregation SP - 616 KW - rank-aggregation KW - ranking AU - Klementiev, Alexandre AU - Roth, Dan AU - Small, Kevin PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-74958-5_60 ER -