Journal of Information Processing Systems

  • 제8권4호
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  • Pages.721-738
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  • 2012
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Proactive: Comprehensive Access to Job Information

  • 투고 : 2012.07.17
  • 심사 : 2012.09.10
  • 발행 : 2012.12.31
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초록

The Internet has become an increasingly important source for finding the right employees, so more and more companies post their job openings on the Web. The large amount and dynamic nature of career recruiting information causes information overload problems for job seekers. To assist Internet users in searching for the right job, a range of research and commercial systems were developed over the past 10 years. Surprisingly, the majority of existing job search systems support just one, rarely two ways of information access. In contrast, our work focused on exploring a value of comprehensive access to job information in a single system (i.e., a system which supports multiple ways). We designed Proactive, a recommendation system providing comprehensive and personalized information access. To assist the varied needs of users, Proactive has four information retrieval methods - a navigable list of jobs, keyword-based search, implicit preference-based recommendations, and explicit preference-based recommendations. This paper introduces the Proactive and reports the results of a study focusing on the experimental evaluation of these methods. The goal of the study was to assess whether all of the methods are necessary for users to find relevant jobs and to what extent different methods can meet different users' information requirements.

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참고문헌

  1. Aktas, M.S., M. Pierce, G.C. Fox, and D. Leake, A Web based Conversational Case-Based Recommender System for Ontology aided Metadata Discovery, in Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing. 2004, IEEE Computer Society. pp.69-75.
  2. Belkin, N.J. and W.B. Croft, Information filtering and information retrieval: two sides of the same coin? Commun. ACM, 1992. 35(12): pp.29-38. https://doi.org/10.1145/138859.138861
  3. Bridge, D. and F. Ricci, Supporting product selection with query editing recommendations, in Proceedings of the 2007 ACM conference on Recommender systems. 2007, ACM: Minneapolis, MN, USA. pp.65-72.
  4. Brusilovsky, P. and C. Tasso, Preface to Special Issue on User Modeling for Web Information Retrieval. User Modeling and User-Adapted Interaction, 2004. 14(2): pp.147-157. https://doi.org/10.1023/B:USER.0000029016.80122.dd
  5. Gadanho, S.C. and N. Lhuillier, Addressing uncertainty in implicit preferences, in Proceedings of the 2007 ACM conference on Recommender systems. 2007, ACM: Minneapolis, MN, USA. pp.97-104.
  6. Kleinberg, J.M., Two algorithms for nearest-neighbor search in high dimensions, in Proceedings of the twenty-ninth annual ACM symposium on Theory of computing. 1997, ACM: El Paso, Texas, United States. pp.599-608.
  7. Lang, S., S. Laumer, C. Maier, and A. Eckhardt, Drivers, challenges and consequences of Erecruiting: a literature review, in Proceedings of the 49th SIGMIS annual conference on Computer personnel research. 2011, ACM: San Antonio, Texas, USA. pp.26-35.
  8. Laumer, S. and A. Eckhardt, Help to find the needle in a haystack: integrating recommender systems in an IT supported staff recruitment system, in Proceedings of the 47th annual conference on Computer personnel research. 2009, ACM: Limerick, Ireland. pp.7-12.
  9. Lee, D.H. and P. Brusilovsky, Fighting Information Overflow with Personalized Comprehensive Information Access: A Proactive Job Recommender, in Proceedings of the Third International Conference on Autonomic and Autonomous Systems. 2007, IEEE Computer Society. p.21.
  10. Lee, I., An architecture for a next-generation holistic e-recruiting system. Commun. ACM, 2007. 50(7): pp.81-85. https://doi.org/10.1145/1272516.1272518
  11. Lee, I., Modeling the benefit of e-recruiting process integration. Decis. Support Syst., 2011. 51(1): pp.230-239. https://doi.org/10.1016/j.dss.2010.12.011
  12. Li, J. and O. Zaiane, Combining Usage, Content, and Structure Data to Improve Web Site Recommendation E-Commerce and Web Technologies, K. Bauknecht, M. Bichler, and B. Proll, Editors. 2004, Springer Berlin / Heidelberg. pp.313-315.
  13. Micarelli, A., F. Gasparetti, F. Sciarrone, and S. Gauch, Personalized search on the world wide web, in The adaptive web, B. Peter, K. Alfred, & N. Wolfgang, Editors. 2007, Springer-Verlag. pp.195-230.
  14. Middleton, S.E., H. Alani, N.R. Shadbolt, and D.C.D. Roure, Exploiting synergy between ontologies and recommender systems, in Semantic Web Workshop 2002, World Wide Web Conference. 2002, Sementic Web Workshop 2002, WWW2002: Hawaii, USA.
  15. Montaner, M., B. Lopez, and J.L.D.L. Rosa, A Taxonomy of Recommender Agents on the Internet. Artif. Intell. Rev., 2003. 19(4): pp.285-330. https://doi.org/10.1023/A:1022850703159
  16. Peter, G. UPRE: User Preference Based Search System. 2006.
  17. Ramli, R., S.A. Noah, and M.M. Yusof, Ontological-based model for human resource decision support system (HRDSS), in Proceedings of the 2010 international conference on On the move to meaningful internet systems. 2010, Springer-Verlag: Hersonissos, Crete, Greece. pp.585-594.
  18. Schafer, J.B., J. Konstan, and J. Riedi, Recommender systems in e-commerce, in Proceedings of the 1st ACM conference on Electronic commerce. 1999, ACM: Denver, CO, United States. pp.158-166.
  19. Schafer, J.B., J.A. Konstan, and J. Riedl, E-Commerce Recommendation Applications. Data Min. Knowl. Discov., 2001. 5(1-2): pp.115-153. https://doi.org/10.1023/A:1009804230409
  20. Singh, A., C. Rose, K. Visweswariah, V. Chenthamarakshan, and N. Kambhatla, PROSPECT: a system for screening candidates for recruitment, in Proceedings of the 19th ACM international conference on Information and knowledge management. 2010, ACM: Toronto, Canada. pp.659-668.
  21. Smyth, B., K. Bradley, and R. Rafter, Personalization techniques for online recruitment services. Commun. ACM, 2002. 45(5): pp.39-40. https://doi.org/10.1145/585597.585599
  22. Tvarozek, M. and M. Bielikova, Adaptive Faceted Browsing in Job Offers. Proceedings in Informatics and Information Technologies: Tools for Acquisition, Organization and Persenting of Information and Knowledge, 2007: pp.149-160.
  23. Viappiani, P., P. Pu, and B. Faltings, Conversational recommenders with adaptive suggestions, in Proceedings of the 2007 ACM conference on Recommender systems. 2007, ACM: Minneapolis, MN, USA. pp.89-96.

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