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Learning Convolutional Ranking-Score Function by Query Preference Regularization

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Intelligent Data Engineering and Automated Learning – IDEAL 2017 (IDEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10585))

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.

The study was supported by the open research program of the Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT, Nanjing University of Information Science and Technology, Nanjing, China, (Grant No. KBDat1602).

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Author information

Authors and Affiliations

  1. Huawei Technologies Co., Ltd., Shanghai, China

    Guohui Zhang

  2. Jiangsu University of Technology, Jiangsu, 213001, China

    Gaoyuan Liang

  3. Reflektion, Inc., San Mateo, 94404, USA

    Weizhi Li

  4. Auburn University, Auburn, 36849, USA

    Jian Fang

  5. Information Technology Service Center, Intermediate People’s Court of Linyi City, Linyi, China

    Jingbin Wang

  6. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Yanyan Geng

  7. Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China

    Jing-Yan Wang

Authors
  1. Guohui Zhang
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  2. Gaoyuan Liang
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  3. Weizhi Li
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  4. Jian Fang
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  5. Jingbin Wang
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  6. Yanyan Geng
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Corresponding author

Correspondence to Jing-Yan Wang .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, United Kingdom

    Hujun Yin

  2. School of Electronic and Electrical Engineering, Nanjing University, Nanjiing, China

    Yang Gao

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Songcan Chen

  4. Guilin University of Electronic Technology, Guilin, China

    Yimin Wen

  5. Guilin University of Electronic Technology, Guilin, China

    Guoyong Cai

  6. Guilin University of Electronic Technology, Guilin, China

    Tianlong Gu

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  8. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

  9. Southeast University, Nanjing, China

    Minling Zhang

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Zhang, G. et al. (2017). Learning Convolutional Ranking-Score Function by Query Preference Regularization. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_1

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68934-0

  • Online ISBN: 978-3-319-68935-7

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