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2017 | OriginalPaper | Buchkapitel

Learning Convolutional Ranking-Score Function by Query Preference Regularization

verfasst von : Guohui Zhang, Gaoyuan Liang, Weizhi Li, Jian Fang, Jingbin Wang, Yanyan Geng, Jing-Yan Wang

Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017

Verlag: Springer International Publishing

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Abstract

Ranking score plays an important role in the system of content-based retrieval. Given a query, the database items are ranked according to the ranking scores in a descending order, and the top-ranked items are returned as retrieval results. In this paper, we propose a new ranking scoring function based on the convolutional neural network (CNN). The ranking scoring function has a structure of CNN, and its parameters are adjusted to both queries and query preferences. The learning process guarantees that the ranking score of the query itself is large, and also the ranking scores of the positives (database items which the query wants to link) are larger than those of the negatives (database items which the query wants to avoid). Moreover, we also impose that the neighboring database items have similar ranking scores. An optimization problem is formulated and solved by Estimation-Maximization method. Experiments over the benchmark data sets show the advantage over the existing learning-to-rank methods.

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Metadaten
Titel
Learning Convolutional Ranking-Score Function by Query Preference Regularization
verfasst von
Guohui Zhang
Gaoyuan Liang
Weizhi Li
Jian Fang
Jingbin Wang
Yanyan Geng
Jing-Yan Wang
Copyright-Jahr
2017
Buch
Intelligent Data Engineering and Automated Learning – IDEAL 2017

Print ISBN: 978-3-319-68934-0

Electronic ISBN: 978-3-319-68935-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-68935-7_1

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