Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering Truyen, Tran The, Phung, Dinh Q. and Venkatesh, Svetha 2011, Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering, in SDM 2011 : Proceedings of the 11th SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, Philadelphia, Pa., pp. 426-437. Attached Files Name Description MIMEType Size Downloads Title Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering Author(s) Truyen, Tran The orcid.org/0000-0001-6531-8907 Phung, Dinh Q. orcid.org/0000-0002-9977-8247 Venkatesh, Svetha Conference name International Conference on Data Mining (11th : 2011 : Mesa, Ariz.) Conference location Mesa, Ariz. Conference dates 28-30 Apr. 2011 Title of proceedings SDM 2011 : Proceedings of the 11th SIAM International Conference on Data Mining Editor(s) [Unknown] Publication date 2011 Conference series International Conference on Data Mining Start page 426 End page 437 Total pages 12 Publisher Society for Industrial and Applied Mathematics Place of publication Philadelphia, Pa. Keyword(s) subsets data mining partitions discrete choice theory parameterisation Summary Ranking is an important task for handling a large amount of content. Ideally, training data for supervised ranking would include a complete rank of documents (or other objects such as images or videos) for a particular query. However, this is only possible for small sets of documents. In practice, one often resorts to document rating, in that a subset of documents is assigned with a small number indicating the degree of relevance. This poses a general problem of modelling and learning rank data with ties. In this paper, we propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on two application areas: (i) document ranking with the data from the recently held Yahoo! challenge, and (ii) collaborative filtering with movie data. The results demonstrate that the models are competitive against well-known rivals. ISBN 9780898719925 Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2011, SIAM Persistent URL http://hdl.handle.net/10536/DRO/DU:30044801 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 01 May 2012, 10:58:56 EST Thu, 03 May 2012, 15:24:10 EST Thu, 03 May 2012, 15:25:43 EST Wed, 29 Jan 2014, 12:56:16 EST Tue, 23 Feb 2016, 13:57:35 EST Thu, 03 Mar 2016, 11:11:11 EST Sat, 17 Jun 2017, 03:10:51 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 263 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 10:58:56 EST

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