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Ontological user profiling in recommender systems

Authors:

Stuart E. Middleton,

Nigel R. Shadbolt,

David C. De RoureAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 22, Issue 1

Pages 54 - 88

https://doi.org/10.1145/963770.963773

Published: 01 January 2004 Publication History

Abstract

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

References

[1]

Alani, H., Dasmahapatra, S., O'hara, K., and Shadbolt, N. 2003. ONTOCOPI---Using ontology-based network analysis to identify communities of practice. IEEE Intell. Syst. 18, 2, 18--25.]]

[2]

Aha, D., Kibler, D., and Albert, M. 1991. Instance-based learning algorithms. Mach. Learn. 6, 37--66.]]

[3]

Balabanović, M. and Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Commun. ACM 40, 3, 67--72.]]

[4]

Billsus, D. and Pazzani, M. J. 2000. User modelling for Adaptive News Access. In User Model. User-Adapt. Interact. 10, 147--180.]]

[5]

Bollacker, K. D., Lawrence, S., and Giles, C. L. 1998. CiteSeer: An autonomous web agent for automatic retrieval and identification of interesting publications. In Autonomous Agents 98 (Minneapolis, Minn.).]]

[6]

Budzik, J., Hammond, K., and Birnbaum, L. 2001. Information access in context. Knowl. Based Syst. 14 (1--2), 37--53.]]

[7]

Burke, R. 2000. Knowledge-based recommender systems. In Encyclopaedia of Library and Information Systems, vol. 69, Supplement 32. A. Kent, Ed.]]

[8]

Claypool, M., Gokhale, A., and Miranda, T. 1999. Combining content-based and collaborative filters in an online newspaper. In Proceedings of the 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99) (Berkeley, Calif.). ACM, New York.]]

[9]

Craven, M., Dipasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam K., and Slattery, S. 1998. Learning to extract symbolic knowledge from the world wide web. In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98).]]

[10]

Delgado, J., Ishii, N., and Ura, T. 1998. Intelligent collaborative information retrieval. In Proceedings of Artificial Intelligence (IBERAMIA'98). Lecture Notes in Artificial Intelligence Series No. 1484.]]

[11]

Eriksson, H., Fergeson, R., Shahr, Y., and Musen, M. 1999. Automatic generation of ontology editors. In Proceedings of the 12th Workshop on Knowledge Acquisition, Modelling, and Management (KAW'99) (Ban, Alberta, Canada).]]

[12]

Freund, Y. and Schapire, R. E. 1996. Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning.]]

[13]

Guarino, N. and Giaretta, P. 1995. Ontologies and knowledge bases: Towards a terminological clarification. In Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, N. Mars, Ed. IOS Press, 25--32.]]

[14]

Guarino, N., Masolo, C., and Vetere, G. 1999. OntoSeek: Content-based access to the web. IEEE Intell. Syst. 14, 3.]]

[15]

Kobsa, A. 1993. User modeling: Recent work, prospects and Hazards. In Adaptive User Interfaces: Principles and Practice, M. Schneider-Hufschmidt, T. Kühme, and U. Malinowski, Eds. North-Holland, Amsterdam, The Netherlands.]]

[16]

Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J. 1997. GroupLens: Applying collaborative filtering to usenet news. Commun. ACM 40, 3, 77--87.]]

[17]

Lang, K. 1995. NewsWeeder: Learning to filter NetNews. In ICML95 Conference Proceedings, 331--339.]]

[18]

Larkey, L. S. 1998. Automatic essay grading using text categorization techniques. In Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval (Melbourne, Australia).]]

[19]

Maltz, D. and Ehrlich, E. 1995. Pointing the way: Active collaborative filtering. In Proceedings of the CHI'95 Human Factors in Computing Systems. ACM. New York.]]

[20]

McCallum, A. K., Nigam, K., Rennie, J., and Seymore, K. 2000. Automating the construction of internet portals with machine learning. Inf. Retri. 3, 2, 127--163.]]

[21]

Melville, P., Mooney, R. J., and Nagarajan, R. 2002. Content-boosted collaborative filtering for improved recommendations. In Proceedings of the 18th National Conference on Artificial Intelligence (AAAI-2002) (Edmonton, Ont., Canada).]]

[22]

Middleton, S. E., Alani, H., Shadbolt, N. R., and de Roure, D. C. 2002. Exploiting synergy between ontologies and recommender systems. In International Workshop on the Semantic Web, Proceedings of the 11th International World Wide Web Conference WWW-2002 (Hawaii).]]

[23]

Middleton, S. E., de Roure, D. C., and Shadbolt, N. R. 2001. Capturing knowledge of user preferences: Ontologies on recommender systems. In Proceedings of the 1st International Conference on Knowledge Capture (K-CAP 2001) (Victoria, B.C., Canada).]]

[24]

Mladenić, D. 1996. Personal WebWatcher: Design and implementation. Tech. Rep. IJS-DP-7472, Department for Intelligent Systems, J. Stefan Institute.]]

[25]

Mladenić, D. and Stefan, J. 1999. Text-learning and related intelligent agents: A survey. IEEE Intell. Syst. 44--54.]]

[26]

Nwana, H. 1996. Software agents: An overview. The Knowl. Eng. Rev. 11, 3, 205--244.]]

[27]

O'hara, K., Shadbolt, N., and Buckingham Shum, S. 2001. The AKT Manifesto. http: www.aktors.org/publications/manifesto.pds.]]

[28]

Porter, M. 1980. An algorithm for suffix stripping. Program. 14, 3, 130--137.]]

[29]

Rashid, A., Albert, I., Cosley, D., Lam, S. K., Mcnee, S. M., Konstan, J. A., and Riedl, J. 2002. Getting to know you: Learning new user preferences in recommender systems. In Proceedings of the IUI'02 (San Francisco, Calif.).]]

[30]

Schein, A. L., Popescul, A., and Ungar, L. H. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the SIGIR'02 (Tampere, Finland).]]

[31]

Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Comput. Surv.]]

[32]

Shadbolt, N., O'hara, K., and Crow, L. 1999. The experimental evaluation of knowledge acquisition techniques and methods: history, problems and new directions. Int. J. Hum.-Comput. Stud. 51, 729--755.]]

[33]

Smart Staff 1974. User's Manual for the SMART Information Retrieval System. Tech. Rep. 71--95, Cornell University.]]

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Index Terms

Ontological user profiling in recommender systems
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
  2. Machine learning
    1. Learning settings
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 22, Issue 1

January 2004

177 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/963770

Issue’s Table of Contents

Copyright © 2004 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2004

Published in TOIS Volume 22, Issue 1

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https://doi.org/10.1117/12.3057747
Chen YLin ZGuo Z(2024)Application of Hierarchical Attention Network in Vulnerability Detection Model2024 2nd International Conference on Big Data and Privacy Computing (BDPC)10.1109/BDPC59998.2024.10649344(43-48)Online publication date: 10-Jan-2024
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Wang HSong Y(2024)Portrait of College Students’ Online Learning Behavior Based on Artificial Intelligence TechnologyIEEE Access10.1109/ACCESS.2024.334944812(6318-6328)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3349448
Al-Hassan MAbu-Salih BAlshdaifat EAloqaily ARodan A(2024)An Improved Fusion-Based Semantic Similarity Measure for Effective Collaborative Filtering RecommendationsInternational Journal of Computational Intelligence Systems10.1007/s44196-024-00429-417:1Online publication date: 14-Mar-2024
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Bahrani PMinaei Bidgoli BParvin HMirzarzaei MKeshavarz A(2023)A Content-Collaborative Recommender System based on clustering and ontologySignal and Data Processing10.61186/jsdp.20.3.19720:3(197-224)Online publication date: 1-Dec-2023
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Liu YZhou ZLi YJin D(2023)Urban Knowledge Graph Aided Mobile User ProfilingACM Transactions on Knowledge Discovery from Data10.1145/359660418:1(1-30)Online publication date: 16-Oct-2023
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Sharma RGopalani DMeena Y(2023)An anatomization of research paper recommender systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105641118:COnline publication date: 1-Feb-2023
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