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International Conference on Database Systems for Advanced Applications

DASFAA 2016: Database Systems for Advanced Applications pp 297–312Cite as

Expert Recommendation in Time-Sensitive Online Shopping

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9642))

Abstract

Like offline shopping mode, when shopping online for the time-sensitive products, people also would like to consult online shopping experts, which can save their time and avoid risk. In this paper, we propose a generalized framework to discover and recommend experts in time-sensitive online shopping. First, we derive the order pattern of users’ purchases in order to establish their relationship. Then we quantify users’ purchase ability and influence acceptance from their historical purchase log. With this knowledge we select accessible users as expert candidates and compute their reputation. Finally, we filter and recommend the experts to users. The experiments on real-world dataset and users show that (1) the accuracy of existing recommendation systems can be improved by embedding the expert discovery process while preserving good coverage; (2) our method achieves a better performance in matching and ranking experts compared with baselines.

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Notes

  1. 1.

    In this study, we apply a Chinese text processing python package, SnowNLP to do sentiment analysis.

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Acknowledgement

The work is supported by National Natural Science Foundation of China (61373022, 61532015, 71473146) and Chinese Major State Basic Research Development 973 Program (2015CB352301).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing, China

    Ming Han & Ling Feng

  2. Centre for Computational Mental Healthcare Research, Institute of Data Science Tsinghua University, Beijing, 100084, China

    Ming Han & Ling Feng

Authors
  1. Ming Han
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  2. Ling Feng
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Corresponding author

Correspondence to Ming Han .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology, Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas, Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota, Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University, Beijing, China

    Xiaoyong Du

  5. Fudan University, Shanghai, China

    X. Sean Wang

  6. Rutgers, The State University of New Jer, New Brunswick, New Jersey, USA

    Hui Xiong

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© 2016 Springer International Publishing Switzerland

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Han, M., Feng, L. (2016). Expert Recommendation in Time-Sensitive Online Shopping. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, X., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9642. Springer, Cham. https://doi.org/10.1007/978-3-319-32025-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-32025-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32024-3

  • Online ISBN: 978-3-319-32025-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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