Skip to main content
SpringerLink
Log in

Team Expansion in Collaborative Environments

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10939))

Included in the following conference series:

  • 3403 Accesses

Abstract

In this paper, we study the team expansion problem in collaborative environments where people collaborate with each other in the form of a team, which might need to be expanded frequently by having additional team members during the course of the project. Intuitively, there are three factors as well as the interactions between them that have a profound impact on the performance of the expanded team, including (1) the task the team is performing, (2) the existing team members, and (3) the new candidate team member. However, the vast majority of the existing work either considers these factors separately, or even ignores some of these factors. In this paper, we propose a neural network based approach TECE to simultaneously model the interactions between the team task, the team members as well as the candidate team members. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://ghtorrent.org/downloads.html.

  2. 2.

    https://cn.aminer.org/billboard/citation.

References

  1. Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Online team formation in social networks. In: WWW, pp. 839–848. ACM (2012)

    Google Scholar 

  2. Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: WSDM, pp. 635–644. ACM (2011)

    Google Scholar 

  3. Baykasoglu, A., Dereli, T., Das, S.: Project team selection using fuzzy optimization approach. Cybern. Syst.: Int. J. 38(2), 155–185 (2007)

    Article  Google Scholar 

  4. Bradley, J.H., Hebert, F.J.: The effect of personality type on team performance. J. Manag. Dev. 16(5), 337–353 (1997)

    Article  Google Scholar 

  5. Chen, S.J., Lin, L.: Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Trans. Eng. Manag. 51(2), 111–124 (2004)

    Article  MathSciNet  Google Scholar 

  6. Cummings, J.N., Kiesler, S.: Who collaborates successfully?: prior experience reduces collaboration barriers in distributed interdisciplinary research. In: CSCW, pp. 437–446. ACM (2008)

    Google Scholar 

  7. Forbes, D.P., Borchert, P.S., Zellmer-Bruhn, M.E., Sapienza, H.J.: Entrepreneurial team formation: an exploration of new member addition. Entrep. Theory Pract. 30(2), 225–248 (2006)

    Article  Google Scholar 

  8. Han, Y., Tang, J.: Who to invite next? Predicting invitees of social groups. In: AAAI, pp. 3714–3720 (2017)

    Google Scholar 

  9. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: WWW, pp. 173–182 (2017)

    Google Scholar 

  10. Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: RecSys, pp. 233–240. ACM (2016)

    Google Scholar 

  11. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  12. Li, L., Tong, H., Cao, N., Ehrlich, K., Lin, Y.R., Buchler, N.: Enhancing team composition in professional networks: problem definitions and fast solutions. IEEE Trans. Knowl. Data Eng. 29(3), 613–626 (2017)

    Article  Google Scholar 

  13. Li, L., Yao, Y., Tang, J., Fan, W., Tong, H.: QUINT: on query-specific optimal networks. In: KDD, pp. 985–994. ACM (2016)

    Google Scholar 

  14. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  15. Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: CIKM, pp. 931–940. ACM (2008)

    Google Scholar 

  16. Rangapuram, S.S., Bühler, T., Hein, M.: Towards realistic team formation in social networks based on densest subgraphs. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1077–1088. ACM (2013)

    Google Scholar 

  17. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461. AUAI Press (2009)

    Google Scholar 

  18. Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)

    Article  Google Scholar 

  19. Soomro, A.B., Salleh, N., Mendes, E., Grundy, J., Burch, G., Nordin, A.: The effect of software engineers? Personality traits on team climate and performance: a systematic literature review. Inf. Softw. Technol. 73, 52–65 (2016)

    Article  Google Scholar 

  20. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)

    Google Scholar 

  21. Tong, H., Faloutsos, C., Pan, J.Y.: Fast random walk with restart and its applications. In: ICDM, pp. 613–622 (2006)

    Google Scholar 

  22. Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: KDD, pp. 1235–1244. ACM (2015)

    Google Scholar 

  23. Wi, H., Oh, S., Mun, J., Jung, M.: A team formation model based on knowledge and collaboration. Expert Syst. Appl. 36(5), 9121–9134 (2009)

    Article  Google Scholar 

  24. Wu, C.Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: WSDM, pp. 495–503. ACM (2017)

    Google Scholar 

  25. Yao, Y., Tong, H., Yan, G., Xu, F., Zhang, X., Szymanski, B.K., Lu, J.: Dual-regularized one-class collaborative filtering. In: CIKM, pp. 759–768. ACM (2014)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61690204, 61672274, 61702252), the National Key Research and Development Program of China (No. 2016YFB1000802), the Fundamental Research Funds for the Central Universities (No. 020214380033), and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Guibing Guo is partially supported by the National Natural Science Foundation for Young Scientists of China (No. 61702084). Hanghang Tong is partially supported by NSF (IIS-1651203, IIS-1715385, CNS-1629888 and IIS-1743040), DTRA (HDTRA1-16-0017), ARO (W911NF-16-1-0168), and gifts from Huawei and Baidu.

Author information

Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Lun Zhao, Yuan Yao, Feng Xu & Jian Lu

  2. Software College, Northeastern University, Shenyang, China

    Guibing Guo

  3. Arizona State University, Tempe, USA

    Hanghang Tong

Authors
  1. Lun Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guibing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hanghang Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan Yao .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, L., Yao, Y., Guo, G., Tong, H., Xu, F., Lu, J. (2018). Team Expansion in Collaborative Environments. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93040-4_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93039-8

  • Online ISBN: 978-3-319-93040-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation