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2018 | OriginalPaper | Buchkapitel

Team Expansion in Collaborative Environments

verfasst von : Lun Zhao, Yuan Yao, Guibing Guo, Hanghang Tong, Feng Xu, Jian Lu

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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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.

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

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

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

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

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)MathSciNetCrossRef

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

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)CrossRef

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

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

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)

11.

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

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)CrossRef

13.

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

14.

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

15.

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

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)

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)

18.

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

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)CrossRef

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)

21.

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

22.

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

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)CrossRef

24.

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

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)

Titel: Team Expansion in Collaborative Environments
verfasst von: Lun Zhao
Yuan Yao
Guibing Guo
Hanghang Tong
Feng Xu
Jian Lu
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93039-8

Electronic ISBN: 978-3-319-93040-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93040-4_56

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