In this work, we propose a self profile adaptation approach to model the task needs of the workers by considering the knowledge activities of workers and the effect of time factor. Moreover, the variations of topic needs over time are also measured by the proposed approach. With the help of topic needs variation, we also propose approaches to find the similar workers by identifying similar topic needs variation over time, and to predict potential task needs with combination of the collaborative profile, i.e., the topic-variation profiles in TP-V method and the document profiles in
TP-D method. In addition, an interface is implemented to show the detailed
information after each operation of proposed methodology mentioned in this work, and two experiments are conducted. From the experimental results, we find that the proposed enhancement of adaptation approach is effective. The TP method which represents the proposed event-based self profile adaptation approach also performs well in the experiments. However, during the period of conducting experiments, we also find there are some limits in our methodology. As a result, these issues are addressed as follows to be further investigated in the future.(1)Improvement of profiling approach: In this work, our proposed self profile
adaptation approach is event-based approach in which a document access triggers the adaptation process. An event-based approach may result in overreaction because workers may not always access the documents they really need. In order to ease the situation of overreaction, a transaction-based profiling approach, which considers documents accessed within a period as a transaction, is worth to be explored. The time weight used to reflect the significance of time variance during the execution of task can be computed by adopting the time cancroids of transactions.(2)Refinement of topic taxonomy: The topic taxonomy used in this work is inherited
from previous research [19], which consists of thirty-six topics. Nevertheless, we find some cases conducting the opposite result to that in experiment 1-1. By using the interface to observe the details, we find that the similar workers identified by our proposed approach can not provide enough support for them, since their relevant topics are not contained in the topic taxonomy. For this reason, the refinement of topic taxonomy continuously with emerging topics is important to model workers’ task needs.Reference
[1] A. Abecker, A. Bernardi, K. Hinkelmann, O. Kuhn, M. Sintek, "Context-aware, proactive delivery of task-specific information: the KnowMore Project,"
Information Systems Frontiers, 2(3/4), pp.253-276, 2000a.
[2] A. Abecker, A. Bernardi, H. Maus, M. Sintek, C. Wenzel, "Information Supply for Business Processes: Coupling Workflow with Document Analysis and Information Retrieval," Knowledge Based Systems, 13(1), pp.271-284, 2000b.
[3] R. Baeza-Yates, B. Ribeiro-Neto, "Modern Information Retrieval," New York: The ACM Press, 1999.
[4] M. Balabanovi'c, "An Adaptive Web Page Recommendation Service," Proceedings of the First International conference on Autonomous Agents, Marina del Rey, CA, pp.378-385, February, 1997.
[5] N.J. Belkin, W.B. Croft, "Information Filtering and Information Retrieval: Two Sides of the Same Coin?" Communications of the ACM, 35(12), pp.29-38, 1992.
[6] P. D. Bra, G. J. Houben, F. Dignum, "Task-Based Information Filtering: Providing Information that is Right for the Job," Proceedings of INFWET97, http://wwwis.win.tue.nl/infwet97/proceedings/task-based.html, 1997.
[7] J.S. Brown, P. Duguid, "Organization Learning and Communities of Practice,"
Organization Science, 2(1), pp.40-57, 1991.
[8] A. Celentano, M. G. Fugini, S. Pozzi,"Knowledge-based document retrieval in office environment: The Kabiria system," ACM Transactions on Information Systems, 13(3), pp.237-268, 1995.
[9] T. H. Davenport, L. Prusak, "Working Knowledge: How Organizations Manage what they know," Boston MA: Harvard Business School Press, 1998.
[10] J. Davies, A. Duke, A. Stonkus, "OntoShare: Using Ontologies for Knowledge Sharing," Proceedings of the 11th International WWW Conference WWW2002, Hawaii, USA, 2002.
[11] K. D. Fenstermacher, C. Marlow, "Supporting Consultants with Task-Specific Information Retrieval," Proceedings of The American Association of Artificial Intelligence, Orlando Florida: AAAI Press, 1999.
[12] K. D. Fenstermacher, "Process-Aware Knowledge Retrieval," Proceedings of the 35th Hawaii International Conference on System Sciences, Big Island, Hawaii, USA, pp.209-217, 2002.
[13] G. Fischer, J. Ostwald, "Knowledge Management: Problems, Promises, Realities, and Challenges," IEEE Intelligent Systems, 16(1), pp.60-73, 2001.
[14] D. Goldberg, B. M. Nichols, Oki, D. Terry, "Using Collaborative Filtering to Weave an Information Tapestry," Communications of the ACM, 35(12), pp.61-70, 1992.
[15] P. H. Gray, "The Impact of Knowledge Repositories on Power and Control in the Workplace," Information Technology & People, 14(4), pp.368-384, 2001.
[16] J. Hahn, M. Subramani, " A Framework of Knowledge Management Systems:
Issues and Challenges for Theory and Practice," Proceedings of the 21st International Conference on Information Systems, Brisbane, Australia, pp.302-312, 2000.
[17] A. Kankanhalli, F. Tanudidjaja, J. Sutanto, C.Y. Tan (Bernard), "The Role of IT in Successful Knowledge Management Initiatives," Communications of ACM, 46(9), pp.69-73, 2003.
[18] J. Koh, Y.-G. Kim, "Knowledge Sharing in Virtual Communities: An e-Business Perspective," Expert Systems with Applications, 26, pp.155-166, 2004.
[19] D.-R. Liu, I.-C. Wu, and K.-S. Yang (2005), "Task-based K-Support System:
Disseminating an Sharing Task-relevant Knowledge," Expert Systems with Applications, 29(2), pp.408-423, 2005.
[20] D. W. McDonald, M.S. Ackerman, "Expertise Recommender: A Flexible Recommendation System and Architecture," Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, Philadelphia, PA, pp.231-240, 2000.
[21] S. E. Middleton, N. R. Shadbolt, D. C. Roure, "Ontological User Profiling in Recommender Systems," ACM Transaction on Information Systems, 22(1), pp.54-88, 2004.
[22] J. Mostafa, S. Mukhopadhyay, W. Lam, M. Palakal, "A Multi-level Approach to Intelligent Information Filtering: Model, System and Evaluation," ACM Transactions on Information Systems, 15(4), pp.368-399, 1997.
[23] S. Mukhopadhyay, J. Mostafa, M. Palakal, "An Adaptive Multi-level Information Filtering System," Proceedings of the Fifth International Conference on User Modeling, pp.21-28, 1996.
[24] I. Nonaka, "A Dynamic Theory of Organizational Knowledge Creation,"
Organization Science, 5(1), pp.14-37,1994.
[25] J. Park, S. Hunting, "XML Topic Maps: Creating and using Topic Maps for the Web," Boston MA: Addison-Wesley, 2003.
[26] M. Pazzani, J. Muramatsu, D. Billus, "Syskill & Webert: Identifying Interesting Web Sites," Proceedings of the Thirteen National Conference on Artificial Intelligence, Portland, Oregon: AAAI Press., pp.54-61, 1996.
[27] M. Pazzani, D. Billsus, "Learning and Revising User Profiles: The Identification of Interesting Web Sites," Machine Learning 27, pp.313-331, 1997.
[28] P. Resnick, H.R. Varian, "Recommender Systems," Communications of the ACM, 40(3), pp.56-58, 1997.
[29] C. J. v. Rijsbergen, "Information Retrieval," Butterworths, London, 1979.
[30] J. J. Rocchio, "Relevance Feedback in Information Retrieval," in: G. Salton (Eds), The SMART Retrieval System: Experiments in Automatic Document Processing, Englewood Cliffs, NJ: Prentice Hall, pp.313-323, 1971.
[31] T. Rodden, "A survey of CSCW Systems," Interacting with Computers, 3(3), pp.319-353, 1991.
[32] G. Salton, C. Buckley, "Term Weighting Approaches in Automatic Text Retrieval,"
Information Processing & Management, 24(5), pp.513-523, 1988.
[33] G. Salton, C. Buckley, "Improving Retrieval Performance by Relevance Feedback,"
Journal of the American Society for Information Science, 41(4), pp.288-297, 1990.
[34] F. Sebastiani, "Machine Learning in Automated Text Categorization: A Survey,"
Technical report, Istituto di Elaborazione dell'Informazione, C.N.R., Pisa, Italy, 1999.
[35] A. Sieg, B. Mobasher, R. Burke, "Inferring User's Information Context: Integrating User Profiles and Concept Hierarchies,", Proceedings of the 2004 Meeting of the International Federation of Classification Societies, Chicago, USA, 2004.
[36] P. L. Wang, D. Soergel, "A Cognitive Model of Document Use during a Research Project. Study I. Document Selection," Journal of the American Society for Information Science and Technology, 49(2), pp.115-133, 1998.
[37] P. L. Wang, M. D. While, "A Cognitive Model of Document Use during a Research Project. Study II. Document at Reading and Citing stage," Journal of The American Society for Information Science and Technology, 50(20), pp.98-114, 1999.
[38] E. Wenger, R. McDemott, W. Snyder, "Cultivating Communities of Practice,"
Harvard Business School Press, 2002.
[39] D. H. Widyantoro, T. R. loerger, J. Yen, "Learning User Interest Dynamics with a Three-Descriptor Representation," Journal of the American Society for Information Science and Technology, 52(3), pp.212-225, 2001.
[40] I.H. Witten, A. Moffat, T.C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images, 2nd edn., Morgan Kaufmann Publishers, Los Alto, USA, 1999.
[41] I-C. Wu, D.-R. Liu, W.-H. Chen, "Task-stage Knowledge Support Model: Coupling User Information Needs with Task Stage Identification," Proceeding of the IEEE 2005 International Conference on Information Reuse and Integration (IRI 2005), Las Vegas, USA, Aug. 2005, pp. 19-24.
[42] M. H. Zack, "Managing Codified Knowledge," Sloan Management Review, 40(4), pp.45-58, 1999.