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dc.contributor.authorLiang, Shangsong
dc.contributor.authorMarkov, Ilya
dc.contributor.authorRen, Zhaochun
dc.contributor.authorde Rijke, Maarten
dc.date.accessioned2021-03-30T12:23:25Z
dc.date.available2021-03-30T12:23:25Z
dc.date.issued2018
dc.identifier.citationLiang, S., Markov, I., Ren, Z., & de Rijke, M. (2018). Manifold Learning for Rank Aggregation. Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18. doi:10.1145/3178876.3186085
dc.identifier.isbn9781450356398
dc.identifier.doi10.1145/3178876.3186085
dc.identifier.urihttp://hdl.handle.net/10754/668406
dc.description.abstractWe address the task of fusing ranked lists of documents that are retrieved in response to a query. Past work on this task of rank aggregation often assumes that documents in the lists being fused are independent and that only the documents that are ranked high in many lists are likely to be relevant to a given topic. We propose manifold learning aggregation approaches, ManX and v-ManX, that build on the cluster hypothesis and exploit inter-document similarity information. ManX regularizes document fusion scores, so that documents that appear to be similar within a manifold, receive similar scores, whereas v-ManX first generates virtual adversarial documents and then regularizes the fusion scores of both original and virtual adversarial documents. Since aggregation methods built on the cluster hypothesis are computationally expensive, we adopt an optimization method that uses the top-k documents as anchors and considerably reduces the computational complexity of manifold-based methods, resulting in two efficient aggregation approaches, a-ManX and a-v-ManX. We assess the proposed approaches experimentally and show that they significantly outperform the state-of-the-art aggregation approaches, while a-ManX and a-v-ManX run faster than ManX, v-ManX, respectively.
dc.description.sponsorshipThis research was supported by Ahold Delhaize, Amsterdam Data Science, the Bloomberg Research Grant program, the Criteo Faculty Research Award program, Elsevier, the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement nr 312827 (VOX-Pol), the Google Faculty Research Awards program, the Microsoft Research Ph.D. program, the Netherlands Institute for Sound and Vision, the Netherlands Organisation for Scientific Research (NWO) under project nrs 612.001.116, CI-14-25, 652.002.001, 612.001.551, 652.001.003, and Yandex. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttp://dl.acm.org/citation.cfm?doid=3178876.3186085
dc.relation.urlhttps://pure.uva.nl/ws/files/40119703/p1735_liang.pdf
dc.rightsArchived with thanks to ACM Press
dc.titleManifold Learning for Rank Aggregation
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.conference.date2018-04-23 to 2018-04-27
dc.conference.name27th International World Wide Web, WWW 2018
dc.conference.locationLyon, FRA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of Amsterdam, Amsterdam, Netherlands
dc.contributor.institutionJD.com, Beijing, China
dc.identifier.pages1735-1744
kaust.personLiang, Shangsong
dc.identifier.eid2-s2.0-85051484984
refterms.dateFOA2021-03-30T12:23:51Z


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