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International Conference on Data Mining and Big Data

DMBD 2017: Data Mining and Big Data pp 9–21Cite as

Mining Relationship Between User Purposes and Product Features Towards Purpose-Oriented Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10387))

Abstract

To help in decision making, buyers in online shopping tend to go through each product features, functionalities, etc. provided by vendors and reviews made by other users, which is not an effective way when confronting loaded of information and especially if the buyers are beginner users who have limited experience and knowledge. To deal with these problems, we propose a framework of purpose-oriented recommendation which present a ranking of products suitable for a designated user purpose by identifying important product features to fulfill the purpose from user reviews. As technical foundation for realizing the framework, we propose several methods to mine relation between user purposes and product features from the online reviews. The experimental results employing reviews of digital cameras in Amazon.com show the effectiveness and stability of proposed methods with acceptable rate of precision and recall.

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Acknowledgment

This work was supported by JSPS KAKENHI Grant Number 15K00423 and the Kayamori Foundation of Informational Science Advancement.

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Authors and Affiliations

  1. Department of Social Informatics, Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, 606-8501, Japan

    Sopheaktra Yong & Yasuhito Asano

Authors
  1. Sopheaktra Yong
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  2. Yasuhito Asano
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Corresponding author

Correspondence to Sopheaktra Yong .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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Yong, S., Asano, Y. (2017). Mining Relationship Between User Purposes and Product Features Towards Purpose-Oriented Recommendation. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-61845-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61844-9

  • Online ISBN: 978-3-319-61845-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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