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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.

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Title Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering
Author(s) Truyen, Tran TheORCID iD for Truyen, Tran The orcid.org/0000-0001-6531-8907
Phung, Dinh Q.ORCID iD for 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
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