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International Conference on Business Information Systems

BIS 2019: Business Information Systems pp 122–136Cite as

Profile Inference from Heterogeneous Data

Fundamentals and New Trends

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 353))

Abstract

One of the essential steps in most business is to understand customers’ preferences. In a data-centric era, profile inference is more and more relaying on mining increasingly accumulated and usually anonymous (protected) data. Personalized profile (preferences) of an anonymous user can even be recovered by some data technologies. The aim of the paper is to review some commonly used information retrieval techniques in recommendation systems and introduce new trends in heterogeneous information network based and knowledge graph based approaches. Then business developers can get some insights on what kind of data to collect as well as how to store and manage them so that better decisions can be made after analyzing the data and extracting the needed information.

The research is supported by Natural Science Foundation of China (NSFC. 11501044), Jiangsu Science and Technology Basic Research Programme (BK20171237), Key Program Special Fund in XJTLU (KSF-E-21), Research Development Fund of XJTLU (RDF-2017-02-23), Research Enhance Fund of XJTLU (REF-18-01-04) and partially supported by NSFC (No. 11571002, 11571047, 11671049, 11671051, 61672003, 11871339).

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Author information

Authors and Affiliations

  1. Department of Matheamtics, Xi’an Jiaotong Liverpool University, Suzhou, China

    Xin Lu, Shengxin Zhu, Qiang Niu & Zhiyi Chen

Authors
  1. Xin Lu
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  2. Shengxin Zhu
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  3. Qiang Niu
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  4. Zhiyi Chen
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Corresponding authors

Correspondence to Shengxin Zhu or Qiang Niu .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

  2. ETSI Informática, University of Seville, Seville, Spain

    Rafael Corchuelo

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Lu, X., Zhu, S., Niu, Q., Chen, Z. (2019). Profile Inference from Heterogeneous Data. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 353. Springer, Cham. https://doi.org/10.1007/978-3-030-20485-3_10

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  • DOI: https://doi.org/10.1007/978-3-030-20485-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20484-6

  • Online ISBN: 978-3-030-20485-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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