Personalizing the Search for Persons: A Recommender-based Approach

Tobias Keim, Jochen Malinowski, Gregor Heinrich, Oliver Wendt



Recommendation systems are widely used on the Internet to assist customers in finding the products or services that best fit their individual preferences. While current implementations successfully reduce information overload by generating personalized suggestions when searching for objects such as books or movies, recommendation systems so far cannot be found in another potential field of application: the personalized search for subjects such as business partners or employees. This is astonishing as (1) the number of CV-, assessment- and social network-data available on the Internet is growing and (2) the complexity and scope of selecting the right partner is much higher than when buying a book. We argue that recommendation systems personalizing the search for people need to be grounded on two pillars: unary attributes on the one hand and relational attributes on the other. We present a framework meeting these requirements together with an outline of a first prototypical implementation.


  1. Crowder, R., Hughes, G. and Hall, W. (2002) Approaches to Locating Expertise Using Corporate Knowledge Int. J. Intell. Sys. Acc. Fin. Mgmt., 11 , 185-200.
  2. DiTomaso, N. (2001) The loose coupling of jobs: the subcontracting of everyone, in Berg, I. and Kalleberg, A.L. (Eds.) Sourcebook of Labor Markets: Evolving Structures and Processes, Kluwer Academic/Plenum, New York, 247-270.
  3. Dunphy, D. and Bryant, B. 1996 Teams: Panaceas or prescriptions for improved performance? Human Relations, 49, 677-699.
  4. Färber, F., Keim, T., and Weitzel, T. (2003) An Automated Recommendation Approach to Personnel Selection, Proceedings of the 2003 Americas Conference on Information Systems, Tampa.
  5. Granovetter, M.S. (1985) Economic Action and Social Structure: the problem of Embeddedness, American Journal of Sociology, Vol. 91, pp. 481 - 510.
  6. Guha, R., Kumar, R., Raghavan, P. and Tomkins, A. (2004) Propagation of Trust and Distrust, Proceedings of the WWW2004-Conference, May 17-22, New York, USA, pp. 403-412.
  7. Graham, M. and Kennedy, J. (2004) Exploring and Examining Assessment Data via a Matrix Visualisation, Proceedings of the AVI 2004, Gallipoli, Lecce, Italy, ACM Press. pp. 158-162.
  8. Heckerman, D., Meek, C., and Koller, D. (2004) Probabilistic Models for Relational Data, Technical Report MSR-TR-2004-30, Microsoft Research.
  9. Heinrich, G. (2004) Teamarbeit nach Mass - Expertisemanagement in Organisationsnetzwerken, Trendkompass Electronic Business - IT-Innovationen & neue Prozesse im Unternehmenseinsatz, IRB-Verlag, Stuttgart.
  10. Hofmann, T. (1999) Probabilistic latent semantic analysis, Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI), July 30-August 1, Stockholm, Sweden, 289-296.
  11. Hofmann, T. and Puzicha, J. (1999) Latent class models for collaborative filtering, Proceedings of the 16th International Joint Conference on Artificial Intelligence, July 31 - August 6, Stockhom, Sweden, 688-693.
  12. Jackson, S.E. (1996) The consequences of diversity in multidisciplinary work teams, in: West, M.A. Handbook of workgroup psychology, John Wiley & Sons, Sussex.
  13. Jensen, D., and Neville, J. (2002) Data Mining in Social Networks, NAS 2002.
  14. Jones, G.R. and George, J.M. (1998) The experience and evolution of Trust: Implications for Co-operation and Teamwork, The Academy of Management Review, 23, 3, pp. 531-546.
  15. Kautz, H., Selman, B. and Shah, M. (1997) Referral Web: Combining Social Networks and Collaborative Filtering, Communications of the ACM, Vol. 40, no. 3, pp. 63-65.
  16. Keim, T., König, W. and von Westarp, F. (2004) Bewerbungspraxis 2005 - Eine empirische Untersuchung mit über 11.000 Stellensuchenden im Internet, Research report, University of Frankfurt, 2004.
  17. Keim, T., König, W., von Westarp, F. , Weitzel, T. and Wendt, O. “Recruiting Trends 2005 - Eine empirische Untersuchung der Top-1000-Unternehmen in Deutschland und von 1000 Unternehmen aus dem Mittelstand in Deutschland“, Research report, University of Frankfurt, 2005.
  18. Keim, T., Weitzel, T. (2005) An Integrated Framework for Online PartnershipBuilding, Proceedings of the 38th Hawaiian International Conference on System Sciences (HICSS-38), Hilton Waikoloa Village, Big Island, Hawaii.
  19. Leicht, K. T. and Marx, J. (1997) The Consequences of Informal Job Finding for Men and Women, Academy of Management Journal, 40, pp. 967-987.
  20. Melville, P., Mooney, R.J. and Nagarajan, R. (2002) Content-boosted collaborative filtering for improved recommendations, Proceedings of the 18th National Conference on Artificial Intelligence, pp. 187-192.
  21. Malone, T. W. and Laubacher, R. J. (1998) The Dawn of the E-Lance Economy, Harvard Business Review, 76 (5), pp. 144-152
  22. Montgomery, J. D. (1991) Social Networks and labor-market outcomes: toward an economic analysis, American Economic Review, 81, pp. 1408-1417.
  23. McCallum, A., Corrada-Emmanuel, A. and Wang, X. (2004) The AuthorRecipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email, Technical Report, UM-CS-2004- 096.
  24. Neville, J., Adler, M. and Jensen, D. (2003) Clustering Relational Data Using Attribute and Link Information, Text-Mining & Link-Analysis Workshop, TextLink.
  25. Popescul, A., Ungar, L.H., Pennock, D.M. and Lawrence, S. (2001) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments, Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, August 2-5, Seattle, USA, pp. 437-444.
  26. Resnick, P. and Varian, H. R. (1997) Recommender systems. Communications of the ACM, 40 (3), pp. 56-58.
  27. Richardson, M., Agrawal, R. and Domingos, P. (2003) Trust management for the semantic web, Proceedings of the Second International Semantic Web Conference, October 20-23, Sanibel Islands, USA, pp. 351-368.
  28. Russo, G., Rietveld, P., Nijkamp, P. and Gorter, C. (2000) Recruitment channel use and applicant arrival: An empirical analysis, Empirical economics, 25, pp. 673-697.
  29. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2000) Analysis of recommendation algorithms for e-commerce, Proceedings of the ACM Conference on Electronic Commerce, pp. 158-167.
  30. Sarwar, B., Karypsis, G., Konstan, J. A. and Riedl, J. (2000) Application of dimensionality reduction in recommender system - A case study, Proceedings of the ACM WebKDD 2000 Web Mining for E-Commerce Workshop, ACM, New York.
  31. Schneider, B., Kristof-Brown, A., Goldstein, H. W. and Smith, D. B. (1997) What is this thing called fit?, in Anderson, N. and Herriot, P. (Eds.): International handbook of Selection and Assessment, John Wiley & Sons, pp. 393-412.
  32. Ungar, L. and Foster, D. (1998) Clustering methods for collaborative filtering, Proceedings of the Workshop on Recommendation Systems, AAAI Press, Menlo Park, California.
  33. Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods and Applications, Cambridge University Press.
  34. Watson-Manheim, M.B., Crowsten, K. and Chudoba, K.M. (2002) Discontinuities and continuities: a new way to understand virtual work, Information Technology & People, Vol. 15(3), pp. 191-209.
  35. Wolfe, A. and Jensen, D. (2004) Playing Multiple Roles, Discovering Overlapping Roles in Social Networks, ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields.
  36. Yolum, P. and Singh, M. P. (2003) Emergent properties of referral systems, Proceedings of the 2nd International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS), ACM Press.

Paper Citation

in Harvard Style

Keim T., Malinowski J., Heinrich G. and Wendt O. (2005). Personalizing the Search for Persons: A Recommender-based Approach . In Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005) ISBN 972-8865-38-4, pages 125-134. DOI: 10.5220/0001421801250134

in Bibtex Style

author={Tobias Keim and Jochen Malinowski and Gregor Heinrich and Oliver Wendt},
title={Personalizing the Search for Persons: A Recommender-based Approach},
booktitle={Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)},

in EndNote Style

JO - Proceedings of the 1st International Workshop on Web Personalisation, Recommender Systems and Intelligent User Interfaces - Volume 1: WPRSIUI, (ICETE 2005)
TI - Personalizing the Search for Persons: A Recommender-based Approach
SN - 972-8865-38-4
AU - Keim T.
AU - Malinowski J.
AU - Heinrich G.
AU - Wendt O.
PY - 2005
SP - 125
EP - 134
DO - 10.5220/0001421801250134