A latent variable ranking model for content-based retrieval
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Torralba_A latent variable.pdf
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Author(s)
Quattoni, Ariadna
Carreras, Xavier
Carreras, Xavier
Date Issued
April 2012
Journal
Advances in Information Retrieval
Publisher
Springer Berlin / Heidelberg
Citation
Quattoni, Ariadna, Xavier Carreras, and Antonio Torralba. “A Latent Variable Ranking Model for Content-Based Retrieval.” Advances in Information Retrieval. Ed. Ricardo Baeza-Yates et al. LNCS Vol. 7224. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. 340–351.
Version
Author's final manuscript
Abstract
Since their introduction, ranking SVM models [11] have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of users of the retrieval system. Most previous ranking models learn either a global combination of elementary similarity functions or a combination defined with respect to a single database item. Instead, we propose a “coarse to fine” ranking model where given a query we first compute a distribution over “coarse” classes and then use the linear combination that has been optimized for queries of that class. These coarse classes are hidden and need to be induced by the training algorithm. We propose a latent variable ranking model that induces both the latent classes and the weights of the linear combination for each class from ranking triplets. Our experiments over two large image datasets and a text retrieval dataset show the advantages of our model over learning a global combination as well as a combination for each test point (i.e. transductive setting). Furthermore, compared to the transductive approach our model has a clear computational advantages since it does not need to be retrained for each test query.
Description
34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. Proceedings
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/978-3-642-28997-2_29
