Augmenting research cooperation in production engineering with data analytics


Understanding how members of a research team cooperate and identifying possible synergies may be crucial for organizational success. Using data-driven approaches, recommender systems may be able to find promising collaborations from publication data. Yet, the outcome of scientific endeavors (i.e. publications) are only produced sparingly in comparison to other forms of data, such as online purchases. In order to facilitate this data in augmenting research cooperation, we suggest to combine data-driven approaches such as text-mining, topic modeling and machine learning with interactive system components in an interactive visual recommendation system. The system leads to an augmented perspective on research cooperation in a network: Interactive visualization analyzes, which cooperation could be intensified due to topical overlap. This allows to reap the benefit of both worlds. First, utilizing the computational power to analyze large bodies of text and, second, utilizing the creative capacity of users to identify suitable collaborations, where machine-learning algorithms may fall short.

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  1. 1.

    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  2. 2.

    Bakalov F, Meurs MJ, König-Ries B, Sateli B, Witte R, Butler G, Tsang A (2013) An approach to controlling user models and personalization effects in recommender systems. In: Proceedings of the 2013 int. conf. on Intelligent user interfaces, ACM, pp 49–56

  3. 3.

    Bennett PN, Kelly D, White RW, Zhang Y (2015) Overview of the special issue on contextual search and recommendation. ACM Trans Inf Syst (TOIS) 33(1):1e

    Article  Google Scholar 

  4. 4.

    Borràs J, Moreno A, Valls A (2014) Intelligent tourism recommender systems: a survey. Expert Syst Appl 41(16):7370–7389

    Article  Google Scholar 

  5. 5.

    Bruns S, Calero Valdez A, Greven C, Ziefle M, Schroeder U (2015) What should I read next? a personalized visual publication recommender system. Human Interface and the management of information. Information and knowledge in context. Springer, Berlin, pp 89–100

    Google Scholar 

  6. 6.

    Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapt Interact 12(4):331–370

    Article  MATH  Google Scholar 

  7. 7.

    Calero Valdez A, Schaar AK, Vaegs T, Thiele T, Kowalski T, Aghassi S, Jansen U, Schulz W, Schuh G, Jeschke S, et al (2014) Scientific cooperation engineering making interdisciplinary knowledge available within research facilities and to external stakeholders. In: Proceedings of the 10th international conference on webometrics, informetrics, and scientometrics (WIS): 15th COLLNET Meeting, Ilmenau, Germany, 3–5 September 2014, pp 77–86

  8. 8.

    Calero Valdez A, Schaar AK, Ziefle M, Holzinger A, Jeschke S, Brecher C (2014b) Using mixed node publication network graphs for analyzing success in interdisciplinary teams. In: Automation, Communication and Cybernetics in Science and Engineering 2013/2014, Springer International Publishing, pp 737–749

  9. 9.

    Calero Valdez A, Brauner P, Schaar AK, Holzinger A, Ziefle M (2015) Reducing complexity with simplicity-usability methods for industry 4.0. In: Proceedings 19th triennial congress of the IEA, vol 9, p 14

  10. 10.

    Calero Valdez A, Bruns S, Greven C, Schroeder U, Ziefle M (2015) What do my colleagues know? dealing with cognitive complexity in organizations through visualizations. In: Learning and collaboration technologies, Springer, pp 449–459

  11. 11.

    Calero Valdez A, Schaar AK, Bender J, Aghassi S, Schuh G, Ziefle M (2016) Social media applications for knowledge exchange in organizations. Innovations in knowledge management. Springer, Berlin, pp 147–176

    Google Scholar 

  12. 12.

    Calero Valdez A, Ziefle M, Verbert K (2016) HCI for recommender systems: the past, the present and the future. In: International Conference on Recommender Systems, RecSys’16 Boston, USA, ACM

  13. 13.

    Calero Valdez A, Ziefle M, Verbert K, Felfernig A, Holzinger A (2016) Recommender systems for health informatics: State-of-the-art and future perspectives. In: Holzinger, A (ed) Machine Learning for Health Informatics, Lecture Notes in Computer Science LNCS 9605, Springer, pp 391–414

  14. 14.

    Conforti R, de Leoni M, La Rosa M, van der Aalst WM, ter Hofstede AH (2015) A recommendation system for predicting risks across multiple business process instances. Decis Support Syst 69:1–19

    Article  Google Scholar 

  15. 15.

    De Clercq M, Stock M, De Baets B, Waegeman W (2016) Data-driven recipe completion using machine learning methods. Trends Food Sci Technol 49:1–13

    Article  Google Scholar 

  16. 16.

    Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10):78–87

    Article  Google Scholar 

  17. 17.

    Drachsler H, Verbert K, Santos OC, Manouselis N (2015) Panorama of recommender systems to support learning. In: Recommender systems handbook, Springer, pp 421–451

  18. 18.

    Feldman R, Sanger J (2007) The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, Cambridge

    Google Scholar 

  19. 19.

    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333

    Article  Google Scholar 

  20. 20.

    Godoy D, Corbellini A (2015) Folksonomy-based recommender systems: a state-of-the-art review. Int J Intell Syst 31(4):314–346

    Article  Google Scholar 

  21. 21.

    Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70

    Article  Google Scholar 

  22. 22.

    Gretarsson B, O’Donovan J, Bostandjiev S, Hall C, Höllerer T (2010) Smallworlds: visualizing social recommendations. In: Computer Graphics Forum, Wiley Online Library, vol 29, pp 833–842

  23. 23.

    Hamann T, Schaar AK, Calero Valdez A, Ziefle M (2016) Strategic knowledge management for interdisciplinary teams-overcoming barriers of interdisciplinary work via an online portal approach. In: International conference on human interface and the management of information. Springer International Publishing, pp 402–413

  24. 24.

    Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer,

  25. 25.

    He C, Parra D, Verbert K (2016) Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Syst Appl 56:9–27

    Article  Google Scholar 

  26. 26.

    Hijikata Y, Kai Y, Nishida S (2012) The relation between user intervention and user satisfaction for information recommendation. In: Proceedings of the 27th annual acm symposium on applied computing. ACM, New York, NY, USA, SAC ’12, pp 2002–2007. doi:10.1145/2245276.2232109

  27. 27.

    Holzinger A (2014) Trends in interactive knowledge discovery for personalized medicine: cognitive science meets machine learning. IEEE Intell Inform Bull 15(1):6–14

    Google Scholar 

  28. 28.

    Holzinger A (2016) Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf 3(2):119–131. doi:10.1007/s40708-016-0042-6

    Article  Google Scholar 

  29. 29.

    Karni Z, Shapira L (2013) Visualization and exploration for recommender systems in enterprise organization. In: IS&T/SPIE electronic imaging. International Society for Optics and Photonics, p 86640E

  30. 30.

    Klerkx J, Verbert K, Duval E (2014) Enhancing learning with visualization techniques. In: Handbook of research on educational communications and technology, Springer, pp 791–807

  31. 31.

    Knijnenburg BP, Reijmer NJ, Willemsen MC (2011) Each to his own: how different users call for different interaction methods in recommender systems. In: Proc. of the Fifth ACM Conf. on recommender systems, ACM, New York, NY, USA, RecSys ’11, pp 141–148. doi:10.1145/2043932.2043960

  32. 32.

    Konstan JA, Riedl J (2012) Recommender systems: from algorithms to user experience. User Model User Adapt Interact 22(1–2):101–123

    Article  Google Scholar 

  33. 33.

    Manouselis N, Drachsler H, Verbert K, Santos OC (2014) Recommender systems for technology enhanced learning: research trends and applications. Springer Science & Business Media

  34. 34.

    McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI’06 extended abstracts on Human factors in computing systems, ACM, pp 1097–1101

  35. 35.

    Miner G (2012) Practical text mining and statistical analysis for non-structured text data applications. Academic Press, Amsterdam

    Google Scholar 

  36. 36.

    Mutlu B, Veas E, Trattner C, Sabol V (2015) Vizrec: a two-stage recommender system for personalized visualizations. In: Proceedings of the 20th international conference on intelligent user interfaces companion, ACM, pp 49–52

  37. 37.

    O’Donovan J, Smyth B, Gretarsson B, Bostandjiev S, Höllerer T (2008) Peerchooser: visual interactive recommendation. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, pp 1085–1088

  38. 38.

    Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072

    Article  Google Scholar 

  39. 39.

    Parra D, Brusilovsky P (2015) User-controllable personalization: a case study with setfusion. Int J Hum Comput Stud 78:43–67

    Article  Google Scholar 

  40. 40.

    Picard RW, Papert S, Bender W, Blumberg B, Breazeal C, Cavallo D, Machover T, Resnick M, Roy D, Strohecker C (2004) Affective learning—a manifesto. BT Technol J 22(4):253–269

    Article  Google Scholar 

  41. 41.

    Pu P, Chen L, Hu R (2012) Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model User Adapt Interact 22(4–5):317–355

    Article  Google Scholar 

  42. 42.

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

    Article  Google Scholar 

  43. 43.

    Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD (2003) Artificial intelligence: a modern approach, vol 2. Upper Saddle River, Prentice hall

  44. 44.

    Rutkin A (2016) Anything you can do. New Sci 229(3065):20–21

    Article  Google Scholar 

  45. 45.

    Sedlmair M, Meyer M, Munzner T (2012) Design study methodology: reflections from the trenches and the stacks. IEEE Trans Vis Comput Gr 18(12):2431–2440

    Article  Google Scholar 

  46. 46.

    Stavrianou A, Brun C (2015) Expert recommendations based on opinion mining of user-generated product reviews. Comput Intell 31(1):165–183

    MathSciNet  Article  Google Scholar 

  47. 47.

    Thiele T, Jooß C, Richert A, Jeschke S (2015) Terminology based visualization of interfaces in interdisciplinary research networks. In: 19th Triennial Congress of the IEA

  48. 48.

    Thiele T, Sommer T, Schröder S, Richert A, Jeschke S (2016) Human-in-the-loop processes as enabler for data analytics in digitalized organizations. In: Mensch und Computer 2016—Workshopbeiträge, MCI Digital Library / Gesellschaft für Informatik e.V

  49. 49.

    Thiele T, Sommer T, Stiehm S, Richert A, Jeschke S (2016) Exploring research networks with data science: a data-driven microservice architecture for synergy detection. In: Proceedings of the 4th international conference on future internet of things and cloud workshops, Vienna, Austria, 22-24 August 2016, pp 246–251

  50. 50.

    Tinghuai M, Jinjuan Z, Meili T, Yuan T, Abdullah AD, Mznah AR, Sungyoung L (2015) Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst 98(4):902–910

    Google Scholar 

  51. 51.

    Verbert K, Parra D, Brusilovsky P, Duval E (2013) Visualizing recommendations to support exploration, transparency and controllability. In: Proceedings of the 2013 international conference on intelligent user interfaces, ACM, pp 351–362

  52. 52.

    Verbert K, Parra D, Brusilovsky P (2016) Agents vs. users: visual recommendation of research talks with multiple dimension of relevance. ACM Trans Interact Intell Syst 6(2):11

    Article  Google Scholar 

  53. 53.

    Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD ’11, pp 448–456. doi:10.1145/2020408.2020480

  54. 54.

    Xiong H, Tan PN, Kumar V (2003) Mining strong affinity association patterns in data sets with skewed support distribution. In: Data mining, 2003. ICDM 2003. Third IEEE International Conference on, IEEE, pp 387–394

  55. 55.

    Yang X, Guo Y, Liu Y, Steck H (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10

    Article  Google Scholar 

  56. 56.

    Yazdi MA, Calero Valdez A, Lichtschlag L, Ziefle M, Borchers J (2016) Visualizing opportunities of collaboration in large research organizations. In: International conference on HCI in Business, Government and Organizations. Springer International Publishing, pp 350–361

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The authors thank the German Research Council DFG for the friendly support of the research in the excellence cluster “Integrative Production Technology in High Wage Countries”. We also thank the reviewers for their constructive feedback on a previous version of this article.

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Correspondence to Thomas Thiele.

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Funded by Deutsche Forschungsgemeinschaft under: DFG EXC-128.

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Thiele, T., Valdez, A.C., Stiehm, S. et al. Augmenting research cooperation in production engineering with data analytics. Prod. Eng. Res. Devel. 11, 213–220 (2017).

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  • Text-mining
  • Topic-modeling
  • Recommender systems
  • Human-computer interaction
  • Interactive machine learning
  • Deep neural networks