A weighted topic model enhanced approach for complementary collaborator recommendation

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

2 Scopus Citations
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Author(s)

  • Chen Yang
  • Xiaoyan Liu
  • Jianshan Sun
  • Thushari Silva
  • Zhongsheng Hua

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Pacific Asia Conference on Information Systems, PACIS 2014
PublisherPacific Asia Conference on Information Systems
Publication statusPublished - Jun 2014

Conference

Title18th Pacific Asia Conference on Information Systems, PACIS 2014
PlaceChina
CityChengdu
Period24 - 28 June 2014

Abstract

Collaborations among interdisciplinary scientists are playing an increasingly important role in science innovations. As it is very difficult for a researcher to master the full knowledge of his/her targeted research areas, how to find suitable collaborators of complementary expertise has turned to be a key factor for researchers to succeed. With the expansion of the Web, the availability of sheer volume of information has resulted in information overload issue and posed significant challenges on determining appropriate scientists to collaborate with effectively for research opportunities. However, current studies on collaborator recommendation ignored this phenomenon and particularly overlooked the complementarity of their expertise within a restrictive context, i.e. for a given funding proposal or a research manuscript draft. In this study we propose a complementary expertise analysis enhanced approach to retrieval experts for research collaboration. It produces recommendation list using a heuristic greedy algorithm based on probabilistic topic model, and generates experts who ought to be complemented in expertise as well as to have good ability. The proposed method has been implemented in ScholarMate research community (www.scholarmate.com). We have conducted a user study to verify the effectiveness of the proposed approach and the preliminary results show its good performance comparing to the benchmarks.

Research Area(s)

  • Complementarity, Latent dirichlet allocation, Recommendation systems, Researcher profiling, Topic models

Citation Format(s)

A weighted topic model enhanced approach for complementary collaborator recommendation. / Yang, Chen; Ma, Jian; Liu, Xiaoyan; Sun, Jianshan; Silva, Thushari; Hua, Zhongsheng.

Proceedings - Pacific Asia Conference on Information Systems, PACIS 2014. Pacific Asia Conference on Information Systems, 2014.

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