Aggregation of partial rankings, p-ratings and top-m lists Export

@inproceedings{citeulike:3478322, abstract = {We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-commerce, learning from experts, analysis of population preference sampling, committee decision making and more. We improve recent constant factor approximation algorithms for aggregation of full rankings and generalize them to partial rankings. Our algorithms improved constant factor approximation with respect to all metrics discussed in Fagin et al's recent important work on comparing partial rankings. We pay special attention to two important types of partial rankings: the well-known top-m lists and the more general p-ratings which we define. We provide first evidence for hardness of aggregating them for constant m, p.}, author = {Ailon, Nir}, citeulike-article-id = {3478322}, citeulike-linkout-0 = {http://serv2.ist.psu.edu:8080/viewdoc/summary;jsessionid=7666C81B3E01D440EF708276595382F5?doi=10.1.1.105.236}, journal = {Symposium on Discrete Algorithms (SODA)}, keywords = {rank-aggregation, ranking}, posted-at = {2008-11-04 11:36:29}, priority = {2}, title = {Aggregation of partial rankings, p-ratings and top-m lists}, url = {http://serv2.ist.psu.edu:8080/viewdoc/summary;jsessionid=7666C81B3E01D440EF708276595382F5?doi=10.1.1.105.236}, year = {2007} }

TY - CONF ID - citeulike:3478322 L3 - citeulike-article-id:3478322 N2 - We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-commerce, learning from experts, analysis of population preference sampling, committee decision making and more. We improve recent constant factor approximation algorithms for aggregation of full rankings and generalize them to partial rankings. Our algorithms improved constant factor approximation with respect to all metrics discussed in Fagin et al's recent important work on comparing partial rankings. We pay special attention to two important types of partial rankings: the well-known top-m lists and the more general p-ratings which we define. We provide first evidence for hardness of aggregating them for constant m, p. JF - Symposium on Discrete Algorithms (SODA) TI - Aggregation of partial rankings, p-ratings and top-m lists KW - rank-aggregation KW - ranking AU - Ailon, Nir PY - 2007/// UR - http://serv2.ist.psu.edu:8080/viewdoc/summary;jsessionid=7666C81B3E01D440EF708276595382F5?doi=10.1.1.105.236 ER -