How to find R&D project opportunities in big data contexts? - Towards personalized information recommendation services

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings of International Conference on Computers and Industrial Engineering, CIE
PublisherCurran Associates Inc.
Pages217-228
Volume1
ISBN (Print)9781629934372
Publication statusPublished - Oct 2013

Publication series

Name
Volume1
ISSN (Electronic)2164-8689

Conference

Title43rd International Conference on Computers and Industrial Engineering 2013, CIE 2013
PlaceHong Kong
CityHong Kong
Period16 - 18 October 2013

Abstract

With the rapid proliferation of online information, how to find useful information, such as suitable jobs, appropriate experts, and proper projects, is really an important problem. Recommendation technique, as one of emerging tools to deal with information overload and information asymmetry, is critically important for providing personalized online information services. With the increase of R&D investment in government and industry, such as high-tech companies and advanced manufacturing enterprises, more and more R&D project information are launched in public websites for cooperation. When the number of online information and users is extremely huge, how to effectively and efficiently recommend R&D project opportunities to related researchers and practitioners is a challenging and complex task. In this paper, a novel two-stage method is proposed for R&D project opportunity recommendation in terms of big data analytics and MapReduce framework. An information filtering method is first offered to identity proper R&D projects as a candidate set. Then, a multi-objective programming with various constraints is suggested to recommend appropriate R&D projects for applicants. The proposed method has been implemented in an online research community - ScholarMate (www.scholarmate.com). The proposed method is an alternative and promising tool for online information recommendation services.

Research Area(s)

  • Big data analytics, Multi-objective programming, Online information services, R&D projects, Recommendation

Citation Format(s)

How to find R&D project opportunities in big data contexts? - Towards personalized information recommendation services. / Xu, Wei; Sun, Jianshan; Ma, Jian; Du, Wei.

Proceedings of International Conference on Computers and Industrial Engineering, CIE. Vol. 1 Curran Associates Inc., 2013. p. 217-228.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review