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Issue No.02 - April-June (2012 vol.3)
pp: 152-164
Siu Hui , Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Baoyao Zhou , IBM Res.-China, Beijing, China
A. C. M. Fong , Sch. of Comput. & Math Sci. (SCMS), Auckland Univ. of Technol. (AUT), Auckland, New Zealand
Jie Tang , Tsinghua Univ., Beijing, China
Guan Hong , Dept. of Comput., Unitec, Auckland, New Zealand
ABSTRACT
The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.
INDEX TERMS
recommender systems, consumer behaviour, data mining, fuzzy logic, Internet, ontologies (artificial intelligence), personalized Web resources recommendation, consumer emotion, behavior analysis, buying behaviors, technology-savvy consumers, semantic Web usage mining approach, periodic Web access patterns discovering, annotated web usage logs, consumer behaviors, self-reporting, behavioral tracking, fuzzy logic, real-life temporal concepts, periodic pattern-based Web access activities, fuzzy temporal representations, resource representations, personal Web usage lattice, personal Web usage ontology, OWL, Ontologies, Semantic Web, Context, Lattices, Association rules, Semantics, semantic web., Emotion and behavior profiling, behavioral tracking, adaptation in mid to long-term interaction, consumer habits, personalization, recommender system, weblog mining, knowledge discovery, ontology generation
CITATION
Siu Hui, Baoyao Zhou, A. C. M. Fong, Jie Tang, Guan Hong, "Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis", IEEE Transactions on Affective Computing, vol.3, no. 2, pp. 152-164, April-June 2012, doi:10.1109/T-AFFC.2011.22
REFERENCES
[1] R.J. Dolan, "Emotion, Cognition, and Behavior," Science, vol. 298, no. 5596, pp. 1191-1194, 2002.
[2] P. Weinberg and W. Gottwald, "Impulsive Consumer Buying as a Result of Emotion," J. Business Research, vol. 10, no. 1, pp. 43-57, 1982.
[3] F. Piron, "A Comparison of Emotional Reactions Experienced by Planned, Unplanned and Impulse Purchasers," Advances in Consumer Research, vol. 20, nos. 341-344, pp. 199-204, 1993.
[4] J. Kalbach, Designing Web Navigation. O'Reilly, 2007.
[5] C. Miao, Q. Yang, H. Fang, and A. Goh, "A Cognitive Approach for Agent-Based Personalized Recommendation," Knowledge-Based Systems, vol. 20, pp. 397-405, 2007.
[6] D.H. Choi and B.S. Ahn, "Eliciting Customer Preferences for Products from Navigation Behavior on the Web: A Multicriteria Decision Approach with Implicit Feedback," IEEE Trans. Systems, Man, and Cybernetics, Part A, vol. 39, no. 4, pp. 880-889, July 2009.
[7] J.H. Janssen, J.N. Bailenson, W.A. IJsselsteijn, and J.H.D.M. Westerin, "Intimate Heartbeats: Opportunities for Affective Communication Technology," IEEE Trans. Affective Computing, vol. 1, no. 2, pp. 72-80, July-Dec. 2010.
[8] J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data," ACM SIGKDD Explorations, vol. 1, no. 2, pp. 12-23, 2000.
[9] "WebTrends Web Analytics," http://www.netiq.comwebtrends, 2011.
[10] B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, "Effective Personalization Based on Association Rule Discovery from Web Usage Data," Proc. Third Int'l Workshop Web Information and Data Management, pp. 9-15, 2001.
[11] J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, "Mining Access Patterns Efficiently from Web Logs," Proc. Fourth Pacific-Asia Conf. Knowledge Discovery and Data Mining, Current Issues and New Applications, pp. 396-407, 2000.
[12] T. Berners-Lee, J. Hendler, and O. Lassila, "The Semantic Web," Scientific Am., vol. 284, no. 5, pp. 34-43, May 2001.
[13] G. Stumme, B. Berendt, and A. Hotho, "Usage Mining for and on the Semantic Web," Proc. NSF Workshop Next Generation Data Mining, pp. 77-86, Nov. 2002.
[14] S. Middleton, N. Shadbolt, and B.D.D. Roure, "Capturing Interest through Inference and Visualization: Ontological User Profiling in Recommender Systems," Proc. Int'l Conf. Knowledge Capture, pp. 62-69, 2003.
[15] R. Missaoui, P. Valtchev, C. Djeraba, and M. Adda, "Toward Recommendation Based on Ontology-Powered Web-Usage Mining," IEEE Internet Computing, vol. 11, no. 4, pp. 45-52, July/Aug. 2007.
[16] A. Sieg, B. Mobasher, and R. Burke, "Web Search Personalization with Ontological User Profiles," Proc. 16th ACM Conf. Conf. Information and Knowledge Management, pp. 525-534, 2007.
[17] A. Maedche and S. Staab, "Ontology Learning for the Semantic Web," IEEE Intelligent Systems, vol. 16, no. 2, pp. 72-79, Mar./Apr. 2001.
[18] A. Todirascu, F. De Beuvron, D. Gâlea, and F. Rousselot, "Using Description Logics for Ontology Extraction," Proc. First Workshop Ontology Learning at the 14th European Conf. Artificial Intelligence, Aug. 2000.
[19] A. Maedche and S. Staab, "Discovering Conceptual Relations from Text," Proc. 14th European Conf. Artificial Intelligence, pp. 321-325, Aug. 2000.
[20] P. Clerkin, P. Cunningham, and C. Hayes, "Ontology Discovery for the Semantic Web Using Hierarchical Clustering," Proc. First Semantic Web Mining Workshop, 2001.
[21] P. Cimiano, S. Staab, and J. Tane, "Deriving Concept Hierarchies from Text by Smooth Formal Concept Analysis," Proc. GI Workshop Lehren-Lernen-Wissen-Adaptivit, pp. 72-79, 2003.
[22] T.T. Quan, S.C. Hui, A.C.M. Fong, and T.H. Cao, "Automatic Fuzzy Ontology Generation for Semantic Web," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 6, pp. 842-856, June 2006.
[23] C. De Maio, G. Fenza, V. Loia, and S. Senatore, "Towards an Automatic Fuzzy Ontology Generation," Proc. IEEE Int'l Conf. Fuzzy Systems, pp. 1044-1049, Aug. 2009.
[24] C. De Maio, G. Fenza, V. Loia, and S. Senatore, "A Multi Facet Representation of a Fuzzy Ontology Population," Proc. IEEE/WIC/ACM Int'l Joint Conf. Web Intelligence and Intelligent Agent Technology, pp. 401-404, 2009.
[25] X. Xu, Y. Wu, and J. Chen, "Fuzzy FCA Based Ontology Mapping," Proc. First Int'l Conf. Networking and Distributed Computing, pp. 181-185, Oct. 2010.
[26] Z.M. Ma, Y. Lv, and L. Yan, "A Fuzzy Ontology Generation Framework from Fuzzy Relational Databases," J. Semantic Web Information Systems, vol. 4, no. 3, pp. 1-15, 2008.
[27] H. Dai and B. Mobasher, "Using Ontologies to Discover Domain-Level Web Usage Profiles," Proc. Second Semantic Web Mining Workshop at ECML/PKDD, 2002.
[28] D. Oberle, B. Berendt, A. Hotho, and J. Gonzalez, "Conceptual User Tracking," Proc. First Int'l Atlantic Web Intelligence Conf., pp. 155-164, May 2003.
[29] M. Eirinaki, M. Vazirgiannis, and I. Varlamis, "SEWeP: Using Site Semantics and a Taxonomy to Enhance the Web Personalization Process," Proc. Ninth ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 99-108, 2003.
[30] R. Meo, P.L. Lanzi, M. Matera, and R. Esposito, "Integrating Web Conceptual Modeling and Web Usage Mining," Proc. WebKDD Workshop Web Mining and Web Usage Analysis, pp. 105-115, 2004.
[31] P. Fraternali, M. Matera, and A. Maurino, "Conceptual-Level Log Analysis for the Evaluation of Web Application Quality," Proc. First Conf. Latin Am. Web Congress, pp. 46-57, Nov. 2003.
[32] D.L. McGuinness and F. Van Harmelen, "OWL Web Ontology Language Overview," W3C Recommendation, http://www. w3.org/TRowl-features/, Feb. 2004.
[33] F. Yergeau, T. Bray, J. Paoli, C.M. Sperberg-McQueen, and E. Maler, "Extensible Markup Language (XML) 1.0 (Third Ed.)," W3C Recommendation, http://www.w3.org/TR/2004REC-xml-20040204 /, Feb. 2004.
[34] G. Klyne and J.J. Carroll, "Resource Description Framework (RDF): Concepts and Abstract Syntax," W3C Recommendation, http://www.w3.org/TRrdf-concepts/, Feb. 2004.
[35] N.F. Noy and D.L. Mcguinness, "Ontology Development 101: A Guide to Creating Your First Ontology," technical report, Stanford Medical Informatics, Palo Alto, Calif., 2001.
[36] Y. Sure, M. Erdmann, J. Angele, S. Staab, R. Studer, and D. Wenke, "OntoEdit: Collaborative Ontology Development for the Semantic Web," Proc. First Int'l Semantic Web Conf. Semantic Web, pp. 221-235, June 2002.
[37] W. Lin, S.A. Alvarez, and C. Ruiz, "Efficient Adaptive-Support Association Rule Mining for Recommender Systems," Data Mining and Knowledge Discovery, vol. 6, no. 1, pp. 83-105, 2002.
[38] M.N. Moreno, F.J. García, M.J. Polo, and V.F. López, "Using Association Analysis of Web Data in Recommender Systems," Proc. Fifth Int'l Conf. E-Commerce and Web Technologies, pp. 11-20, Aug. 2004.
[39] L.A. Zadeh, "Fuzzy Logic and Approximate Reasoning," Synthese, vol. 30, pp. 407-428, 1975.
[40] C. Wong, S. Shiu, and S. Pal, "Mining Fuzzy Association Rules for Web Access Case Adaptation," Proc. Fourth Int'l Conf. Case-Based Reasoning, Case-Based Reasoning Research and Development, pp. 213-220, 2001.
[41] S.C.K. Shiu and S.K. Pal, "Case-Based Reasoning: Concepts, Features and Soft Computing," Applied Intelligence, vol. 21, no. 3, pp. 233-238, 2004.
[42] R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. 11th Int'l Conf. Data Eng., pp. 3-14, Mar. 1995.
[43] L.A. Zadeh, "Fuzzy Sets," J. Information and Control, vol. 8, pp. 338-353, 1965.
[44] R. Cooley, B. Mobasher, and J. Srivastava, "Data Preparation for Mining World Wide Web Browsing Patterns," Knowledge and Information Systems, vol. 1, no. 1, pp. 5-32, 1999.
[45] D. Olaru and B. Smith, "Modelling Daily Activity Schedules with Fuzzy Logic," Proc. 10th Int'l Conf. Travel Behaviour Research, 2003.
[46] G. Stumme, R. Taouil, Y. Bastide, N. Pasquier, and L. Lakhal, "Computing Iceberg Concept Lattices with TITANIC," Data Knowledge Eng., vol. 42, no. 2, pp. 189-222, 2002.
[47] L. Nourine and O. Raynaud, "A Fast Algorithm for Building Lattices," Information Processing Letters, vol. 71, nos. 5/6, pp. 199-204, 1999.
[48] R. Godin, R. Missaoui, and H. Alaoui, "Incremental Concept Formation Algorithms Based on Galois (Concept) Lattices," Computational Intelligence, vol. 11, pp. 246-267, 1995.
[49] E. Bozsak, M. Ehrig, S. Handschuh, A. Hotho, A. Maedche, B. Motik, D. Oberle, C. Schmitz, S. Staab, L. Stojanovic, N. Stojanovic, R. Studer, G. Stumme, Y. Sure, J. Tane, R. Volz, and V. Zacharias, "KAON—Towards A Large Scale Semantic Web," Proc. Third Int'l Conf. E-Commerce and Web Technologies, pp. 304-313, Sept. 2002.
[50] B. Zhou, S.C. Hui, and A.C.M. Fong, "Efficient Sequential Access Pattern Mining for Web Recommendations," Int'l J. Knowledge-Based and Intelligent Eng. Systems, vol. 10, no. 2, pp. 155-168, 2006.
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