| SortNet: Learning to Rank by a Neural Preference Function. | |
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MedLine Citation:
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PMID: 21775258 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms. |
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Authors:
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Leonardo Rigutini; Tiziano Papini; Marco Maggini; Franco Scarselli |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-7-18 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: - ISSN: 1941-0093 ISO Abbreviation: - Publication Date: 2011 Jul |
Date Detail:
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Created Date: 2011-7-21 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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