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A Streaming Approach to Neural Team Formation Training

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14608))

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Abstract

Predicting future successful teams of experts who can effectively collaborate is challenging due to the experts’ temporality of skill sets, levels of expertise, and collaboration ties, which is overlooked by prior work. Specifically, state-of-the-art neural-based methods learn vector representations of experts and skills in a static latent space, falling short of incorporating the possible drift and variability of experts’ skills and collaboration ties in time. In this paper, we propose (1) a streaming-based training strategy for neural models to capture the evolution of experts’ skills and collaboration ties over time and (2) to consume time information as an additional signal to the model for predicting future successful teams. We empirically benchmark our proposed method against state-of-the-art neural team formation methods and a strong temporal recommender system on datasets from varying domains with distinct distributions of skills and experts in teams. The results demonstrate neural models that utilize our proposed training strategy excel at efficacy in terms of classification and information retrieval metrics. The codebase is available at https://github.com/fani-lab/OpeNTF/tree/ecir24.

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Notes

  1. 1.

    https://aminer.org/citation.

  2. 2.

    https://imdb.com/interfaces/.

  3. 3.

    https://uspto.gov/ip-policy/economic-research/research-datasets.

  4. 4.

    https://codelabs.developers.google.com/codelabs/bigquery-github.

References

  1. Arkin, E., Hassin, R.: Minimum diameter covering problems. Networks 36, 147–155 (2000)

    Google Scholar 

  2. Askari, G., Asghri, N., Gordji, M.E., Asgari, H., Filipe, J.A., Azar, A.: The impact of teamwork on an organization’s performance: a cooperative game’s approach. Mathematics 8(10), 1–15 (2020)

    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. Bernabé, R.B., Navia, I.A., García-Peñalvo, F.J.: Faat: freelance as a team. In: Proceedings of the 3rd International Conference on Technological Ecosystems for Enhancing Multiculturality, TEEM 2015, pp. 687–694, New York, NY, USA. Association for Computing Machinery (2015)

    Google Scholar 

  5. Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 1613–1622, Lille, France, 07–09 Jul 2015. PMLR (2015)

    Google Scholar 

  6. Bursic, K.M.: Strategies and benefits of the successful use of teams in manufacturing organizations. IEEE Trans. Eng. Manage. 39(3), 277–289 (1992)

    Article  Google Scholar 

  7. Chen, S.-J., Li, 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  Google Scholar 

  8. Craig, M., McKeown, D.: How to build effective teams in healthcare. Nurs. Times 111(14), 16–18 (2015)

    Google Scholar 

  9. Dashti, A., Samet, S., Fani, H.: Effective neural team formation via negative samples. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM 2022, pp. 3908–3912, New York, NY, USA. Association for Computing Machinery (2022)

    Google Scholar 

  10. Dashti, A., Saxena, K., Patel, D., Fani, H.: OpeNTF: a benchmark library for neural team formation. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, 17–21 October 2022, pp. 3913–3917. ACM (2022)

    Google Scholar 

  11. Durfee, E.H., Boerkoel, J.C., Sleight, J.: Using hybrid scheduling for the semi-autonomous formation of expert teams. Future Gener. Comput. Syst. 31, 200–212 (2014). Special Section: Advances in Computer Supported Collaboration: Systems and Technologies

    Google Scholar 

  12. Fani, H., Bagheri, E., Du, W.: Temporal latent space modeling for community prediction. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12035, pp. 745–759. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45439-5_49

    Chapter  Google Scholar 

  13. Fitzpatrick, E., Askin, R.G.: Forming effective worker teams with multi-functional skill requirements. Comput. Ind. Eng. 48(3), 593–608 (2005)

    Article  Google Scholar 

  14. Gu, Y., et al.: Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthcare 3(1) 2021

    Google Scholar 

  15. Hall, K., et al.: The science of team science: a review of the empirical evidence and research gaps on collaboration in science. Am. Psychol. 73, 532–548 (2018)

    Article  Google Scholar 

  16. Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: ACL 2016 (2016)

    Google Scholar 

  17. Jia, H., Liden, R.C.: Making a difference in the teamwork: linking team prosocial motivation to team processes and effectiveness. Acad. Manag. J. 58, 1102–1127 (2014)

    Google Scholar 

  18. A., Kairgalievna, Zayed, N.M.: The effect of teamwork on employee productivity (2021)

    Google Scholar 

  19. Kargar, M., An, A.: Discovering top-k teams of experts with/without a leader in social networks. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 985–994 (2011)

    Google Scholar 

  20. Kargar, M., An, A.: Efficient top-k keyword search in graphs with polynomial delay. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 1269–1272 (2012)

    Google Scholar 

  21. Kargar, M., Golab, L., Srivastava, D., Szlichta, J., Zihayat, M.: Effective keyword search over weighted graphs. IEEE Trans. Knowl. Data Eng. 34(2), 601–616 (2022)

    Article  Google Scholar 

  22. Kargar, M., Zihayat, M., An, A.: Finding affordable and collaborative teams from a network of experts. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 587–595. SIAM (2013)

    Google Scholar 

  23. Karp, R.M.: Reducibility among Combinatorial Problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds) Complexity of Computer Computations. The IBM Research Symposia Series, pp. 85–103. Springer, Boston (1972). https://doi.org/10.1007/978-1-4684-2001-2_9

  24. Keane, P., Ghaffar, F., Malone, D.: Using machine learning to predict links and improve steiner tree solutions to team formation problems - a cross company study. Appl. Netw. Sci. 5(1), 57 (2020)

    Article  Google Scholar 

  25. Khan, A., Golab, L., Kargar, M., Szlichta, J., Zihayat, M.: Compact group discovery in attributed graphs and social networks. Inf. Process. Manag. 57(2), 102054 (2020)

    Article  Google Scholar 

  26. Kou, Y., et al.: Efficient team formation in social networks based on constrained pattern graph. In: 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, 20–24 April 2020, pp. 889–900. IEEE (2020)

    Google Scholar 

  27. Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: SIGKDD 2009, pp. 467–476. ACM (2009)

    Google Scholar 

  28. Leahey, E.: From sole investigator to team scientist: trends in the practice and study of research collaboration. Ann. Rev. Sociol. 42(1), 81–100 (2016)

    Article  Google Scholar 

  29. Li, C.-T., Shan, M.-K., Lin, S.-D.: On team formation with expertise query in collaborative social networks. Knowl. Inf. Syst. 42(2), 441–463 (2015)

    Article  Google Scholar 

  30. Liao, S., Liang, S., Meng, Z., Zhang, Q.: Learning dynamic embeddings for temporal knowledge graphs. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, WSDM 2021, pp. 535–543, New York, NY, USA. Association for Computing Machinery (2021)

    Google Scholar 

  31. McCormick, T.H., Lee, H., Cesare, N., Shojaie, A., Spiro, E.S.: Using twitter for demographic and social science research: tools for data collection and processing. Sociol. Methods Res. 46(3), 390–421 (2017)

    Article  MathSciNet  Google Scholar 

  32. Neshati, M., Beigy, H., Hiemstra, D.: Expert group formation using facility location analysis. Inf. Process. Manag. 50(2), 361–383 (2014)

    Article  Google Scholar 

  33. Rad, R.H., Bagheri, E., Kargar, M., Srivastava, D., Szlichta, J.: Retrieving skill-based teams from collaboration networks. In: SIGIR 2021: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, 11–15 July 2021, pp. 2015–2019. ACM (2021)

    Google Scholar 

  34. Rad, R.H., Bagheri, E., Kargar, M., Srivastava, D., Szlichta, J.: Subgraph representation learning for team mining. In: WebSci 2022: 14th ACM Web Science Conference 2022, Barcelona, Spain, 26–29 June 2022, pp. 148–153. ACM (2022)

    Google Scholar 

  35. Rad, R.H., Fani, H., Bagheri, E., Kargar, M., Srivastava, D., Szlichta, J.: A variational neural architecture for skill-based team formation. ACM Trans. Inf. Syst. (2023). Just Accepted

    Google Scholar 

  36. Rad, R.H., Fani, H., Kargar, M., Szlichta, J., Bagheri, E.: Learning to form skill-based teams of experts. In: CIKM 2020, pp. 2049–2052. ACM (2020)

    Google Scholar 

  37. Rad, R.H., Mitha, A., Fani, H., Kargar, M., Szlichta, J., Bagheri, E.: PyTFL: a python-based neural team formation toolkit. In: Demartini, G., Zuccon, G., Culpepper, J.S., Huang, Z., Tong, H. (eds.) CIKM 2021: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, 1–5 November 2021, pp. 4716–4720. ACM (2021)

    Google Scholar 

  38. Rad, R.H., Seyedsalehi, S., Kargar, M., Zihayat, M., Bagheri, E.: A neural approach to forming coherent teams in collaboration networks. In: Proceedings of the 25th International Conference on Extending Database Technology, EDBT 2022, Edinburgh, UK, March 29 - April 1, 2022, pp. 2:440–2:444. OpenProceedings.org (2022)

    Google Scholar 

  39. Rahmanniyay, F., Yu, A.J., Seif, J.: A multi-objective multi-stage stochastic model for project team formation under uncertainty in time requirements. Comput. Ind. Eng. 132, 153–165 (2019)

    Article  Google Scholar 

  40. Rosen, M.A., et al.: Teamwork in healthcare: key discoveries enabling safer, high-quality care. Am. Psychol. 73(4), 433–450 (2018). Cited by: 297. All Open Access, Green Open Access

    Google Scholar 

  41. Sapienza, A., Goyal, P., Ferrara, E.: Deep neural networks for optimal team composition. Front. Big Data 2, 14 (2019)

    Article  Google Scholar 

  42. Sherer, P.D.: Leveraging human assets in law firms: human capital structures and organizational capabilities. ILR Rev. 48(4), 671–691 (1995)

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. WSDM 2017, pp. 495–503, New York, NY, USA. Association for Computing Machinery (2017)

    Google Scholar 

  45. Yang, D.-N., Chen, Y.-L., Lee, W.-C., Chen, M.-S.: On social-temporal group query with acquaintance constraint. Proc. VLDB Endow. 4(6), 397–408 (2011)

    Article  Google Scholar 

  46. Younglove-Webb, J., Gray, B., Abdalla, C.W., Thurow, A.P.: The dynamics of multidisciplinary research teams in academia. Rev. High. Educ. 22(4), 425–440 (1999)

    Article  Google Scholar 

  47. ARMEN Zzkarian and Andrew Kusiak: Forming teams: an analytical approach. IIE Trans. 31(1), 85–97 (1999)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Windsor, Windsor, ON, Canada

    Hossein Fani, Reza Barzegar, Arman Dashti & Mahdis Saeedi

Authors
  1. Hossein Fani
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  2. Reza Barzegar
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  3. Arman Dashti
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  4. Mahdis Saeedi
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Corresponding author

Correspondence to Hossein Fani .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Fani, H., Barzegar, R., Dashti, A., Saeedi, M. (2024). A Streaming Approach to Neural Team Formation Training. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14608. Springer, Cham. https://doi.org/10.1007/978-3-031-56027-9_20

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  • DOI: https://doi.org/10.1007/978-3-031-56027-9_20

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