Concept-based learning of human behavior for customer relationship management

Information Sciences

Volumn 181, Issue 10, 2011, Pages 2016-2035

  Galitsky, Boris ^a   De La Rosa, Josep Lluis ^a  

^a UNIVERSITY OF GIRONA   (Spain)

Author keywords

Behavior of human agents; Communicative actions; Concept learning; Multi agent conflict

Indexed keywords

BEHAVIOR OF HUMAN AGENTS; CAUSAL RELATIONSHIPS; COMMUNICATIVE ACTIONS; COMPARATIVE ANALYSIS; CONCEPT LEARNING; CONCEPT-BASED; CONTEXT INFORMATION; CUSTOMER COMPLAINTS; CUSTOMER RELATIONSHIP MANAGEMENT; CUSTOMER SUPPORT; DIRECTED GRAPHS; FALSE NEGATIVES; GRAPH STRUCTURES; HUMAN AGENT; HUMAN ATTITUDES; HUMAN BEHAVIORS; HUMAN INTERACTIONS; MACHINE-LEARNING; MULTI-AGENT CONFLICT; NEAREST-NEIGHBOR APPROACHES; NEGATIVE EXAMPLES; PARTIAL MATCHING;

CUSTOMER SATISFACTION; LEARNING ALGORITHMS; PUBLIC RELATIONS; SALES;

BEHAVIORAL RESEARCH;

EID: 79952183710     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2010.08.027     Document Type: Article

References (69)

1. K. Bach, and R.M. Harnish Linguistic Communication and Speech Acts 1979 MIT Press Cambridge, Mas.
2. J. Barwise Admissible Sets and Structures 1975 Springer Verlag
3. J. Barwise, and John Perry Situations and Attitudes 1983 MIT Press Cambridge, MA, London
4. J. Barwise, and Jerry Seligman Information Flow in Distributed Systems Tracts in Theoretical Computer Science 2000 Cambridge University Press
5. M.E. Bratman Intention, Plans and Practical Reason 1987 Harvard University Press
6. M. Bichler, C. Kiss, A Comparison of Logistic Regression, k-Nearest Neighbor, and Decision Tree Induction for Campaign Management, in: Tenth Americas Conference on Information Systems (AMCIS), New York, 2004.
7. J. Coble, D. Cook, R. Rathi, and L. Holder Iterative structure discovery in graph-based data Int. J. Artif. Intell. Tech. 1-2 14 2005 101 124
8. P.R. Cohen, H.J. Levesque, Performatives in a Rationally Based Speech Act Theory, in: Proceedings of the 28th Conference of Association for Computational Linguistics, 1990, pp. 79-88.
9. M. Davidow Organizational responses to customer complaints: what works and what doesn't J. Service Res. 5 3 2003 225 250
10. T. Dierkes, Martin Bichler, Ramayya Krishnan, Modelling network effects with markov logic networks for churn prediction in the telecommunication industry, in: Fifth Symposium on Statistical Challenges in Electronic Commerce Research, Pittsburgh, PA, May 30 and 31, 2009.
11. Dver, The future of CRM Technology, vol. 13, Thomson Media, 2003, pp. 22-27 (number 7).
12. V.K. Finn, Plausible reasoning in systems of JSM-type, Itogi Nauki I Techniki, Seriya Informatika, vol. 15, 1991, pp. 54-101 (in Russian).
13. V.K. Finn, On the synthesis of cognitive procedures and the problem of induction, NTI Series, 2 N1-2 8-45.12., 1999.
14. A. Formica Ontology-based concept similarity in formal concept analysis Inform. Sci. 176 18 2006 2624 2641 22 (Pubitemid 43946423)
15. GTF, Gtf inspire complaint processing, 2010. .
16. B. Galitsky, A. Miller, Determining possible criminal behaviour of mobile phone users by means of analysing the location tracking data, in: AAAI SSS 2005 on Homeland Security.
17. B. Galitsky, S. Kuznetsov, M. Samokhin, Analyzing conflicts with concept-based learning, in: International Conference on Concept Structures ICCS, 2005.
18. B. Galitsky, B. Kovalerchuk, S.O. Kuznetsov, Learning common outcomes of communicative actions represented by labeled graphs, in: International Conference on Concept Structures, Sheffield, UK, July 22-27, LNCS, vol. 4604, 2006, pp. 387-400. (Pubitemid 350271735)
19. B. Galitsky, and S.O. Kuznetsov Learning communicative actions of conflicting human agents J. Exp. Theor. Artif. Intell. 20 4 2008 277 317
20. B. Galitsky, S.O. Kuznetsov, Scenario argument structure vs individual claim defeasibility: what is more important for validity assessment? in: Interanational Conference on Concept Structures ICCS 2008, LNCS, vol. 5113, 2008, pp. 282-296.
21. B. Galitsky Reasoning about mental attitudes of complaining customers Knowl.-Based Syst. 19 7 2006 592 615 (Pubitemid 44557045)
22. B.A. Galitsky, S.O. Kuznetsov, and D.V. Vinogradov Applying hybrid reasoning to mine for associative features in biological data J. Biomed. Inf. 40 3 2007 203 220 (Pubitemid 46720257)
23. B.A. Galitsky, María Paula González, and Carlos Iván Chesñevar A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues Decis. Supp. Syst. 46 3 2009 717 729
24. B. Ganter, and S. Kuznetsov Pattern structures and their projections Proceedings of the Ninth International Conference on Conceptual Structures, ICCS'01 G. Stumme, H. Delugach, Lecture Notes in Artificial Intelligence 2120 2001 129 142 (Pubitemid 33314664)
25. B. Ganter, and R. Wille Formal Concept Analysis, Mathematical Foundations 1999 Springer
26. M.R. Garey, and D.S. Johnson Computers and Intractability: A Guide to the Theory of NP-Completeness 1979 Freeman San Francisco, CA
27. L. Holder, D. Cook, J. Coble, and M. Mukherjee Graph-based relational learning with application to security Fundam. Inform. (Special Issue on Mining Graphs, Trees and Sequences) 1-2 6 2005 83 101 (Pubitemid 41218093)
28. S. Jayachandran, Subhash Sharma, Peter Kaufman, and Pushkala Raman The role of relational information processes and technology use in customer relationship management J. Market. 69 4 2005 177 192 (Pubitemid 41504947)
29. V.G. Kaburlasos, G.X. Ritter, Computational intelligence based on lattice theory, Studies in CI, vol. N67, 2007.
30. B.-Y. Kang, Dae-Won Kim, and Sang-Jo Lee Exploiting concept clusters for content-based information retrieval Inform. Sci. 170 2-4 2005 443 462 (Pubitemid 40183418)
31. J. Kolodner Case-Based Reasoning 1993 Morgan Kaufmann
32. S.O. Kuznetsov Learning of simple conceptual graphs from positive and negative examples Proceedings of the Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD'99 Lecture Notes in Artificial Intelligence vol. 1704 1999 384 392
33. S.O. Kuznetsov, M.V. Samokhin, Learning closed sets of labeled graphs for chemical applications, ILP 2005, 2005, pp. 190-208. (Pubitemid 41479993)
34. R. Laza, J.M. Corchado, CBR-BDI agents in planning, in: Symposium on Informatics and Telecommunications (SIT'02), Sevilla, Spain, September 25-27, 2002, pp. 181-192.
35. T.A. Letsche, and Michael W. Berry Large-scale information retrieval with latent semantic indexing Inform. Sci. 100 1-4 1997 105 137
36. J.S. Mill, (1843) A system of logic, racionative and inductive. London.
37. T. Mitchell, Machine Learning, in: J.M. Robson (Ed.), Collected Works of John Stuart Mill, Vol. VII, University of Toronto Press, Toronto and Buffalo; Routledge and Kegan Paul, London, 1973.
38. T.M. Mitchell, R.M. Keller, and S.T. Kedar-Cabelli Explanation-based generalization: a unifying view Mach. Learn. 1 1986 47 80
39. MasterControl. MasterControl: customer complaint handling software, 2009. .
40. M. Montaner, Beatriz López, Josep Lluís de la Rosa, Opinion-based filtering through trust, Cooperative Information Agents VI LNCS 2446, 2002, pp. 127-144.
41. S. Muggleton Inductive logic programming: issues, results and the challenge of learning language in logic Artif. Intell. 114 1-2 1999 283 296 (Pubitemid 129588328)
42. H.J. Muller, R. Dieng, Computational Conflicts Conflict Modeling for Distributed Intelligent Systems 2000 Springer-Verlag New York
43. E.W.T. Ngai, L. Xiu, and D.C.K. Chau Application of data mining techniques in customer relationship management Expert Syst. Appl. 36 2009 2592 2602
44. Oracle, Oracle customer relationship management, 2009a. .
45. C. Olivia, C.F. Chang, C.F. Enguix, A.K. Ghose, Case-based bdi agents an effective approach for intelligent search on the world wide web, Intelligent agents in cyberspace, in: AAAI Spring Symposium, 1999.
46. Bo Pang, L.A. Lee, Sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts, in: ACL 2004.
47. B. Pang, L. Lee, S. Vaithyanathan, Thumbs up? sentiment classification using machine learning techniques, in: Proceedings of EMNLP, 2002.
48. S.J. Russell Preliminary steps toward the automation of induction Proceedings of the Fifth National Conference on Artificial Intelligence 1986 Morgan Kaufmann Los Altos, CA 477 484
49. E. Riloff, Automatically generating extraction patterns from untagged text, in: Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96), 1996, pp. 1044-1049.
50. M. Richardson, and Pedro Domingos Markov logic networks Mach. Learn. 62 1-2 2006 107 136 (Pubitemid 43202307)
51. J. Searle Speech Acts. An Essay in the Philosophy of Language 1969 Cambridge University Press Cambridge, Eng.
52. J. Sowa Conceptual Graphs, Conceptual Structures Information Processing in Mind and Machine 1984 Addison-Wesley Reading, MA
53. P. Stone, and M. Veloso Multiagent systems a survey from a machine learning perspective Auton. Rob. 8 3 2000 345 383
54. R.S. Swift Accelerating Customer Relationships: Using CRM and Relationship Technologies 2001 Prentice Hall London, UK
55. M. Turoff, S.R. Hiltz, M. Bieber, J. Fjermestad, and A. Rana Collaborative discourse structures in computer mediated group communications J. Comput.-Mediated Commun. 4 4 1999
56. P.D. Turney, Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews, in: 40th ACL, New Brunswick, N.J., 2002.
57. D. Vinogradov, Logic programs for quazi-axiomatic theories, Nauchno-Tehnicheskaya Informacia Ser 2 N2, 1999, pp. 61-64 (In Russian).
58. G. Weiss, and S. Sen Adaptation and learning in multiagent systems Lecture Notes in Artificial Intelligence vol. 1042 1996 Springer-Verlag Berlin, Heidelberg, New York
59. Wiebe, J.T. Wilson, M. Bell, Identifying collocations for recognizing opinions, in: Proceedings of ACL/EACL 01 Workshop on Collocation, Toulouse, France, July 2001.
60. S.T. Yuan, and W.L. Chang Mixed-initiative synthesized learning approach for web-based CRM Expert Syst. Appl. 20 2 2001 187 200
61. A. Yuksel, U. Kilinc, and F. Yuksel Cross-national analysis of hotel customers' attitudes toward complaining and their complaining behaviours Tourism Manage. 27 1 2006 11 24 (Pubitemid 41554629)
62. Y.Y. Yao, Concept formation and learning: a cognitive informatics perspective, in: Proceedings of the Third IEEE International Conference on Cognitive Informatics (ICCI'04).
63. J.C. Valle-Lisboa, and E. Mizrajia The uncovering of hidden structures by latent semantic analysis Inform. Sci. 177 19 2007 4122 4147 1 (Pubitemid 47058094)
64. S.Y.W. Su SAM: a semantic association model for corporate and scientific-statistical databases Inform. Sci. 29 2-3 1983 151 199
65. H. Zirtiloǧlu, Pinar Yolum, Ranking semantic information for e-government: complaints management, in:Proceedings of the First ACM International Workshop on Ontology-Supported Business Intelligence, Karlsruhe, Germany, October 27, 2008, pp. 1-7.
66. B. Liu, M. Hu, J. Cheng, Opinion observer: analyzing and comparing opinions on the Web, in: 14th WWW Conference, 2005, pp. 342-351.
67. B. Pang, and L. Lee Opinion mining and sentiment analysis Found. Trends Inform. Retrieval 2 1-2 2008 1 135
68. P. Chaovalit, L. Zhou, Movie review mining: a comparison between supervised and unsupervised classification approaches, in: Proceedings of the Hawaii International Conference on System Sciences (HICSS), 2005, p. 112. (Pubitemid 41543998)
69. H.-T. Zheng, Bo-Yeong Kang, and Hong-Gee Kim Exploiting noun phrases and semantic relationships for text document clustering Inform. Sci. 179 13 2009 2249 2262