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Statistical user model supported by R-Tree structure

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Abstract

This paper is about developing a group user model able to predict unknown features (attributes, preferences, or behaviors) of any interlocutor. Specifically, for systems where there are features that cannot be modeled by a domain expert within the human computer interaction. In such cases, statistical models are applied instead of stereotype user models. The time consumption of these models is high, and when a requisite of bounded response time is added most common solution involves summarizing knowledge. Summarization involves deleting knowledge from the knowledge base and probably losing accuracy in the medium-term. This proposal provides all the advantages of statistical user models and avoids knowledge loss by using an R-Tree structure and various search spaces (universes of users) of diverse granularity for solving inferences with enhanced success rates. Along with the formalization and evaluation of the approach, main advantages will be discussed, and a perspective for its future evolution is provided. In addition, this paper provides a framework to evaluate statistical user models and to enable performance comparison among different statistical user models.

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References

  1. Adomavicius G, Tuzhilin A (2007) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

    Article  Google Scholar 

  2. Ahn JJ, Byun HW, Oh KJ, Kim TY (2012) Bayesian forecaster using class-based optimization. Appl Artif Intell 36(3):553–563. doi:10.1007/s10489-011-0275-2

    Article  Google Scholar 

  3. Albrecht D, Zukerman I (2007) Introduction to the special issue on statistical and probabilistic methods for user modeling. In: User modeling and user-adapted interaction

    Google Scholar 

  4. Amento B, Terveen L, Hix D, Ju P (1999) An empirical evaluation of user interfaces for topic management of web sites. In: Proceedings of the conference on human factors in computing systems (CHI ’99). ACM, New York, pp 552–559

    Google Scholar 

  5. Badi R, Bae S, Moore JM, Meintanis K, Zacchi A, Hsieh H, Shipman F, Marshall CC (2006) Recognizing user interest and document value from reading and organizing activities in document triage. In: Proceedings of the 11th int. conference on intelligent user interfaces (IUI). ACM, New York, pp 218–225

    Google Scholar 

  6. Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval. Addison-Wesley, Reading

    Google Scholar 

  7. Bahrampour S, Moshiri B, Salahshoor K (2011) Weighted and constrained possibilistic C-means clustering for online fault detection and isolation. Appl Artif Intell 35(2):269–284. doi:10.1007/s10489-010-0219-2

    Article  Google Scholar 

  8. Baraglia R, Silvestri F (2007) Dynamic personalization of web sites without user intervention. Commun ACM 50(2):63–67

    Article  Google Scholar 

  9. Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the 15th national conference on artificial intelligence, Madison, WI, pp 714–720

    Google Scholar 

  10. Baus J, Kruger A, Wahlster W (2002) A resource-adaptive mobile navigation system. In: Proceedings of the 7th international conference on intelligent user interfaces (IUI). ACM, New York, pp 15–22

    Google Scholar 

  11. Bessa A, Laender AHF, Veloso A, Ziviani N (2012) Alleviating the sparsity problem in recommender systems by exploring underlying user communities

  12. Billsus D, Pazzani M (1998) Learning collaborative information filters. In: Procs. of the 15th international conference on machine. Learning (ICML ’98)

    Google Scholar 

  13. Billsus D, Pazzani M (2000) User modeling for adaptive news access. User Model User-Adapt Interact 10(2–3):147–180

    Article  Google Scholar 

  14. Billsus D, Brunk CA, Evans C, Gladish B, Pazzani M (2002) Adaptive interfaces for ubiquitous web access. Commun ACM 45(5):34–38

    Article  Google Scholar 

  15. Birukov A, Blanzieri E, Giorgini P (2005) Implicit: a recommender system that uses implicit knowledge to produce suggestions. In: Proceedings of the workshop on multi-agent information retrieval and recommender systems at the 19th international joint conference on artificial intelligence (IJCAI)

    Google Scholar 

  16. Breese J, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative clustering. In: Proc. of the fourteenth annual conference on uncertainty in artificial intelligence, San Francisco, p 43

    Google Scholar 

  17. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370. doi:10.1023/A:1021240730564

    Article  MATH  Google Scholar 

  18. Calle FJ, Albacete E, Olaziregi G, Sánchez E, Valle Agudo D, Rivero J, Cuadra D (2011) Cognos: a pragmatic annotation toolkit for the acquisition of natural interaction knowledge. In: 27th conference of the Spanish society for natural language processing, Huelva (Spain)

    Google Scholar 

  19. Castaño L, Calle FJ, Cuadra D, Castro E (2011) User modeling for human-like interaction. In: The 2nd international workshop on user modeling and adaptation for daily routines (UMADR), pp 23–34

    Google Scholar 

  20. Chin D (2001) Empirical evaluation of user models and user-adapted systems. In: User modeling and user-adapted interaction, pp 181–194

    Google Scholar 

  21. Cosley D, Lam SK, Albert I, Konstan JA, Riedl J (2003) Is seeing believing? How recommender interfaces affect users’ opinions. CHI Lett 5

  22. De Meo P, Quattrone G, Rosaci D, Ursino F (2012) Bilateral semantic negotiation: a decentralized approach to ontology enrichment in open multi-agent systems. Int J Data Mining Model Manag 4(1):1–38

    Article  Google Scholar 

  23. Eyharabide V, Amandi A (2012) Ontology-based user profile learning. Appl Artif Intell 36(4):857–869. doi:10.1007/s10489-011-0301-4

    Article  Google Scholar 

  24. Finin T, Drager D (1986) GUMS: a general user modeling system. In: Proceedings of the workshop on strategic computing natural language. association for computational linguistics, Stroudsburg, PA, USA, pp 224–230

    Google Scholar 

  25. Formoso V, Fernández D, Cacheda F, Carneiro V (2012) Using profile expansion techniques to alleviate the new user problem. In: Information processing and management, ISSN 0306-4573

    Google Scholar 

  26. Ghazanfar MA, Prugel-Bennett A (2011) Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution. In: International conference on information systems and computational intelligence, Harbin, China, pp 18–20

    Google Scholar 

  27. Guttman A (1984) R-trees: a dynamic index structure for spatial searching. In: ACM SIGMOD int. conference on management of data. ACM, New York, pp 47–57

    Google Scholar 

  28. Heckerman D, Chickering DM, Meek C, Rounthwaite R, Kadie C (2000) Dependency networks for inference, collaborative filtering, and data visualization. J Mach Learn Res 1:49–75

    Google Scholar 

  29. Herlocker JL, Konstan JA, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr 5:287–310

    Article  Google Scholar 

  30. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  31. Hill W, Stead L, Rosenstein M, Furnas GW (1995) Recommending and evaluating choices in a virtual community of use. In: Proceedings of ACM CHI’95 conference on human factors in computing systems. ACM, New York, pp 194–201

    Google Scholar 

  32. Krulwich B (1997) Lifestyle finder: intelligent user profiling using large-scale demographic data. Artif Intell Mag 18(2):37–45

    Google Scholar 

  33. Larson HJ (1969) Introduction to probability theory and statistical inference. Wiley, New York

    Google Scholar 

  34. Linden G, Smith B, York J (2003) Amazon.com Recommendations: item-to-Item collaborative filtering. IEEE Internet Comput, Jan–Feb

  35. Miller B, Albert I, Lam SK, Konstan J, Riedl J (2003) MovieLens unplugged: experiences with a recommender system on four mobile devices. In: Proceedings of the 17th annual human-computer interaction conference (HCI 2003), British HCI Group, September

    Google Scholar 

  36. Özyer T, Alhajj R (2009) Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer. Appl Artif Intell 31(3):318–331. doi:10.1007/s10489-008-0129-8

    Article  Google Scholar 

  37. Peddy CC, Armentrout D (2003) Building solutions with Microsoft commerce server 2002. Microsoft Press

  38. Perrault CR, Allen JF, Cohen PR (1978) Speech acts as a basis for understanding dialogue coherence. Report 78-5, Department of Computer Science, University of Toronto, Canada

  39. Popescul A, Ungar LH, Pennock DM, Lawrence S (2001) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the 17th conference in uncertainty in artificial intelligence (UAI ’01), pp 437–444

    Google Scholar 

  40. Resnick P, Iakovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of net news. In: Furuta R, Neuwirth C (eds) Proceedings of the 1994 conference on computer. supported collaborative work. ACM, New York, pp 175–186

    Google Scholar 

  41. Rich E (1979) User modeling via stereotypes. Cogn Sci 3(4):329–354

    Article  Google Scholar 

  42. Rosaci D (2007) CILIOS connectionist inductive learning and inter-ontology similarities for recommending information agents. Inf Syst 32(6):793–825

    Article  Google Scholar 

  43. Rosaci D, Sarnè GML, Garruzzo S (2009) MUADDIB: a distributed recommender system supporting device adaptivity. ACM Trans Inf Syst. 27(4):24

    Article  Google Scholar 

  44. Rosaci D, Sarnè GML (2012) A multi-agent recommender system for supporting device adaptivity in e-commerce. J Intell Inf Syst 38(2):393–418

    Article  Google Scholar 

  45. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web (WWW ’01), pp 285–295

    Google Scholar 

  46. Shardanand U, Maes P (1995) Social information filtering: algorithms for automating word of mouth. In: Proceedings of ACM CHI’95 conference on human factors in computing systems. ACM, New York, pp 210–217

    Google Scholar 

  47. Sheth B, Maes P (1993) Evolving agents for personalized information filtering. In: Proceedings of the 9th IEEE conference on artificial intelligence for applications

    Google Scholar 

  48. Siriporn S, Hwajoon K (2009) Modified fuzzy ants clustering approach. Appl Artif Intell 31(2):122–134. doi:10.1007/s10489-008-0117-z

    Article  Google Scholar 

  49. Tsai CF, Yeh HF, Chang JF, Liu NH (2010) PHD: an efficient data clustering scheme using partition space technique for knowledge discovery in large databases. Appl Artif Intell 33(1):39–53. doi:10.1007/s10489-010-0239-y

    Article  Google Scholar 

  50. Ting CY, Phon-Amnuaisuk S (2012) Properties of Bayesian student model for INQ PRO. Appl Artif Intell 36(2):391–406. doi:10.1007/s10489-010-0267-7

    Article  Google Scholar 

  51. Wade W (2003) A grocery cart that holds bread, butter and preferences. NY Times, Jan 16, 2003

  52. Yu K, Schwaighofer A, Tresp V, Xu X, Kriegel HP (2004) Probabilistic memory-based collaborative filtering. IEEE Trans Knowl Data Eng 16(1):56–69

    Article  Google Scholar 

  53. Yuan W, Guan D, Lee YK, Lee S (2011) The small-world trust network. Appl Artif Intell 35(3):399–410. doi:10.1007/s10489-010-0230-7

    Article  Google Scholar 

  54. Zhang Y, Callan J, Minka T (2002) Novelty and redundancy detection in adaptive filtering. In: Proceedings of the 25th annual international ACM SIGIR conference, pp 81–88

    Google Scholar 

  55. Zukerman I, Albretch W (2001) Predictive statistical models for user modeling. User Model User-Adapt Interact 11:1–2

    Article  Google Scholar 

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Acknowledgements

This proposal development belongs to the research projects Thuban (TIN2008-02711), MA2VICMR (S2009/TIC-1542) and Cadooh (TSI-020302-2011-21), supported respectively by the Spanish Ministry of Education and the Spanish Ministry of Industry, Tourism and Commerce.

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  1. Computer Science Department, Carlos III University of Madrid, Madrid, Spain

    Javier Calle, Leonardo Castaño, Elena Castro & Dolores Cuadra

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  1. Javier Calle
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  2. Leonardo Castaño
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  4. Dolores Cuadra
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Correspondence to Leonardo Castaño.

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Calle, J., Castaño, L., Castro, E. et al. Statistical user model supported by R-Tree structure. Appl Intell 39, 545–563 (2013). https://doi.org/10.1007/s10489-013-0432-x

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