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Context-Aware Recommender Systems Influenced by the Users’ Health-Related Data

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User Modeling and Adaptation for Daily Routines

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

This chapter provides an overview of past and current developments in the area of recommender systems, paying special attention to two concepts that we view as cornerstones to provide effective assistance to people during their daily lives: context awareness and health awareness. We will enumerate different dimensions of context that are handled nowadays to maximize the value of the information delivered to the users, and then explain the existing approaches to take health-related data into consideration. Finally, we will describe the main features of a mobile application we are developing that interacts with electronic health record repositories and manages location information to recommend commercial products to the users.

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Notes

  1. 1.

    http://www.openehr.org

  2. 2.

    http://www.w3.org/TR/owl-time/

References

  1. Adomavicius G, Mobasher B, Ricci F, Tuzhilin A (2011) Context-aware recommender systems. AI Mag 32(3):117–142

    Google Scholar 

  2. Adomavicius G, Tuzhilin A (2005) Towards the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6): 739–749

    Article  Google Scholar 

  3. Aghabozorgi SR, Wah TY (2009) Recommender systems: incremental clustering on web log data. In: Proceedings of the 2nd international conference on interaction sciences: information technology, culture and human, Seoul, South Korea

    Google Scholar 

  4. Alam S (2011) Intelligent web usage clustering based recommender system. In: Proceedings of the 5th ACM conference on recommender systems. ACM, New York, pp 367–370

    Google Scholar 

  5. Amatriain X, Jaimes A, Oliver N, Pujol JM (2011) Data mining methods for recommender systems. In: Mathematik für Ingenieure. Springer, Berlin

    Google Scholar 

  6. Anand SS, Mobasher B (2007) Contextual recommendation. Lect Notes Artif Intell 4737: 142–160

    Google Scholar 

  7. Ardissono L, Gena C, Torasso P, Bellifemine F, Chiarotto A, Difino A, Negro B (2004) User modeling and recommendation techniques for personalized electronic program guides. In: Personalized digital television. Targeting programs to individual users. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  8. Asnicar F, Tasso C (1997) IfWeb: a prototype of user-models-based intelligent agent for document filtering and navigation in the World Wide Web. In: Proceedings of the 6th international conference on user modeling, Chia Laguna, Italy

    Google Scholar 

  9. Baek SH, Choi EC, Huh JD (2007) Design of information management model for sensor based context-aware service in ubiquitous home. In: Proceedings of the international conference on convergence information technology, Gyeongju, South Korea

    Google Scholar 

  10. Bakalov F, König-Ries B, Nauerz A, Welsch M (2008) Ontology-based multidimensional personalization modeling for the automatic generation of mashups in next-generation portals. In: Proceedings of the 1st international workshop on ontologies in interactive systems, Liverpool, UK, pp 75–82

    Google Scholar 

  11. Balabanović M, Shoham Y (1997) Fab: content-based collaborative recommender. Commun ACM 40(3):66–72

    Article  Google Scholar 

  12. Baldauf M, Dustdar S, Rosenberg F (2007) A survey on context-aware systems. Int J Ad Hoc Ubiquitous Comput 2(4):263–277

    Article  Google Scholar 

  13. 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

    Google Scholar 

  14. Bates PJ (2003) A study into TV-based interactive learning to the home. http://www.pjb.co.uk/t-learning/contents.htm

  15. Blanco-Fernndez Y, Lpez-Nores M, Gil-Solla A, Ramos-Cabrer M, Pazos-Arias JJ (2011) Exploring synergies between content-based filtering and spreading activation techniques in knowledge-based recommender systems. Inf Sci 181(21):4823–4846

    Article  Google Scholar 

  16. Blanco-Fernndez Y, Lpez-Nores M, Pazos-Arias JJ, Gil-Solla A, Ramos-Cabrer M (2010) Exploiting digital TV users’ preferences in a tourism recommender system based on semantic reasoning. IEEE Trans Consum Electron 56(2):904–912

    Article  Google Scholar 

  17. Blanco-Fernndez Y, Lpez-Nores M, Pazos-Arias JJ, Martn-Vicente MI Automatic generation of mashups for personalized commerce in digital TV by semantic reasoning. In: Proceedings of the 10th international conference on electronic commerce and web technologies, Linz, Austria

    Google Scholar 

  18. Blanco-Fernández Y, Pazos-Arias JJ, López-Nores M, Gil-Solla A, Ramos-Cabrer M (2006) AVATAR: an improved solution for personalized TV based on semantic inference. IEEE Trans Consum Electron 52(1):223–231

    Article  Google Scholar 

  19. Branting LK (2004) Learning feature weights from customer return-set selections. Knowl Inf Syst 6(2):188–202

    Article  Google Scholar 

  20. Bridge D, Göker M, McGinty L, Smyth B (2006) Case-based recommender systems. Knowl Eng Rev 20(3):315–320

    Article  Google Scholar 

  21. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12(4):331–370

    Article  MATH  Google Scholar 

  22. Burkow TM (2008) An easy to use and affordable home-based personal ehealth system for chronic disease management based on free open source software. In: Proceedings of the 21th international congress of the European Federation for Medical Informatics, Göteborg, Sweden, pp 83–88

    Google Scholar 

  23. di Flora C, Ficco M, Russo S, Vecchio V (2005) Indoor and outdoor location-based services for portable wireless devices. In: 25th international conference on distributed computing systems, Columbus, pp 244–250

    Google Scholar 

  24. Dourish P (2004) What we talk about when we talk about context. Pers Ubiquitous Comput 8(1):19–30

    Article  Google Scholar 

  25. Fernández-Luque L, Karlsen R, Vognild LK (2009) Challenges and opportunities of using recommender systems for personalized health education. In: Proceedings of the 22nd international congress of the European Federation for Medical Informatics, Sarajevo, Bosnia and Herzegovina

    Google Scholar 

  26. Ferreira-Satler M, Romero F, Olivas J, Serrano-Guerrero J (20011) Sistema de recomendación de información clínica electrónica basado en ontologías borrosas y perfiles de usuario. In: Proceedings of the conference of the Spanish Association of Artificial Intelligence (CAEPIA), Tenerife, Spain

    Google Scholar 

  27. Flury T, Privat G, Ramparany F (2004) OWL-based location ontology for context-aware services. In: Proceedings of the artificial intelligence in mobile systems (AIMS), Nottingham, UK, pp 52–58

    Google Scholar 

  28. Freyne J, Berkovsky S (2010) Intelligent food planning: personalized recipe recommendation. In: Proceedings of the international conference on intelligent user interfaces (IUI), Hong Kong, China

    Google Scholar 

  29. Freyne J, Berkovsky S, Smith G (2010) Evaluating recommender systems for supportive technologies. In: User modeling and adaptation for daily routines. Springer, London

    Google Scholar 

  30. Ganesan P, Garcia-Molina H, Widom J (2003) Exploiting hierarchical domain structure to compute similarity. ACM Trans Inf Syst 21(1):64–93

    Article  Google Scholar 

  31. Guo X, Lu J (2007) Intelligent e-government services with personalized recommendation techniques. Int J Intell Syst 22(5):401–417

    Article  Google Scholar 

  32. Güler NF, Übeyli ED (2002) Theory and applications of biotelemetry. J Med Syst 26(3): 199–220

    Article  Google Scholar 

  33. Hammer S, Kim J, André E (2010) MED-StyleR: METABO diabetes lifestyle recommender. In: Proceedings of the 4th ACM conference on recommender systems, Barcelona, Spain

    Google Scholar 

  34. Hepp. M, Leukel J, Schmitz V (2007) A quantitative analysis of product categorization standards: content, coverage and maintenance of eCl@ss, UNSPSC, eOTD, and the RosettaNet Technical Dictionary. Knowl Inf Syst 13(1):77–114

    Article  Google Scholar 

  35. Hoens T, Blanton M, Chawla N (2010) Reliable medical recommendation systems with patient privacy. In: Proceedings of the 1st ACM international health informatics symposium (IHI), Arlington, VA

    Google Scholar 

  36. Hristova A, Bernardos AM, Casar JR (2008) Context-aware services for ambient-assisted living: a case study. In: Proceedings of the 1st international symposium on applied sciences on biomedical and communication technologies, pp 1–5

    Google Scholar 

  37. Im KH, Park SC (2007) Case-based reasoning and neural network based expert system for personalization. Expert Syst Appl 32(1):77–85

    Article  Google Scholar 

  38. Kanawati R, Karoui H (2009) A P2P collaborative bibliography recommender system. In: Proceedings of the 4th international conference on internet and web applications and services. Springer, Berlin, pp 90–96

    Google Scholar 

  39. Kenteris M, Gavalas D, Mpitziopoulos A (2010) A mobile tourism recommender system. In: Proceedings of the IEEE symposium on computers and communications, pp 840–845

    Google Scholar 

  40. Kim JH, Lee JH, Park JS, Lee YH, Rim KW (2009) MED-StyleR: METABO diabetes lifestyle recommender. In: Proceedings of the 4th international conference on computer sciences and convergence information technology (ICCIT), Seoul, South Korea

    Google Scholar 

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

    Google Scholar 

  42. Lamber P, Ludwig B, Ricci F, Zini F, Mitterer M (2011) Message-based patient guidance in day-hospital. In: Proceedings of the 12th IEEE international conference on mobile data management (MDM), Lulea, Sweden

    Google Scholar 

  43. Lampropoulos AS, Lampropoulou PS, Tsihrintzis GA (2011) A cascade-hybrid music recommender system for mobile services based on musical genre classification and personality diagnosis. Multimed Tools Appl 59(1):241–258

    Article  Google Scholar 

  44. Lampropoulou PS, Lampropoulos AS, Tsihrintzis GA (2009) A mobile music recommender system based on a two-level genre-rating SVM classifier enhanced by collaborative filtering. Stud Comput Intell 226:361–368

    Article  Google Scholar 

  45. Lin P, Yang F, Yu X, Xu Q (2008) Personalized e-commerce recommendation based on ontology. In: Proceedings of the international conference on internet computing in science and engineering, pp 201–206

    Google Scholar 

  46. Linton F, Schaefer HP (2000) Recommender systems for learning: building user and expert models through long-term observation of application use. User Model User-Adapt Interact 10(2–3):181–208

    Article  Google Scholar 

  47. Lundell J, Hayes T, Vurgun S, Ozertem U, Kimel J, Kaye J, Guilak F, Pavel M (2007) Continous activity monitoring and intelligent contextual prompting to improve medication adherence. In: Proceedings of the 29th international conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France

    Google Scholar 

  48. Luo H, Fan J, Keim DA (2008) Personalized news video recommendation. In: Proceedings of the 16th ACM international conference on multimedia

    Google Scholar 

  49. López-Nores M, Blanco-Fernández Y, Pazos-Arias JJ, García-Duque J (2012) The iCabiNET system: harnessing electronic health record standards from domestic and mobile devices to support better medication adherence. Comput Stand Inter 34(1):109–116

    Article  Google Scholar 

  50. López-Nores M, Pazos-Arias JJ, Garcáa-Duque J, Blanco-Fernández Y (2010) MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning. Knowl Inf Syst 22(1):101–128

    Article  Google Scholar 

  51. López-Nores M, Rey-López M, Pazos-Arias JJ, García-Duque J, Blanco-Fernández Y, Gil-Solla A, Díaz-Redondo RP, Fernández-Vilas A, Ramos-Cabrer M (2009) Spontaneous interaction with audiovisual contents for personalized e-commerce over digital TV. Expert Syst Appl 36(3p1):4192–4197

    Google Scholar 

  52. López-Nores M, Blanco-Fernández Y, Pazos-Arias JJ, Gil-Solla A (2012) Property-based collaborative filtering for health-aware recommender systems. Expert Syst Appl 39(8): 7451–7457

    Article  Google Scholar 

  53. Manjunath BS, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description language. Wiley, Hoboken

    Google Scholar 

  54. Manouselis N, Drachsler H, Vuorikari R, Hummel H, Koper R Recommender systems in technology enhanced learning. In: Proceedings of the 4th ACM conference on recommender systems, pp 203–213

    Google Scholar 

  55. Masthoff J (2010) Group recommender systems: combining individual models. In: Recommender systems handbook. Springer, Heidelberg, pp 677–702

    Google Scholar 

  56. Montaner M, López B, de la Rosa JL (2003) A taxonomy of recommender agents on the internet. Artif Intell Rev 19(4):285–330

    Article  Google Scholar 

  57. Panescu D (2008) Emerging technologies: wireless communication systems for implantable medical devices. Eng Med Biol Mag 27(2):196–101

    Google Scholar 

  58. Panniello U, Tuzhilin A, Gorgoglione M, Palmisano C, Pedone A Experimental comparison of pre- vs post-filtering approaches in context-aware recommender systems. In: Proceedings of the 3rd ACM conference on recommender systems, pp 265–268

    Google Scholar 

  59. Pattaraintakorn P, Zaverucha GM, Cercone N (2007) Web-based health recommender system usign rough sets, survival analysis and rule-based expert systems. Lect Notes Artif Intell 4482: 491–499

    Google Scholar 

  60. Pazzani M (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5):393–408

    Article  Google Scholar 

  61. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27:313–331

    Article  Google Scholar 

  62. Rada R, Mili H, Bicknell E, Blettnet M (1989) Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 19(1):17–30

    Article  Google Scholar 

  63. Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11(4):95–130

    MATH  Google Scholar 

  64. Richardson L, Ruby S (eds) (2007) RESTful web services. O’Reilly Media, Sebastopol

    Google Scholar 

  65. Rosaci D, Sarn G (2008) A multi-agent recommender system for supporting device adaptivity in e-commerce. Stud Comput Intell 162:293–298

    Article  Google Scholar 

  66. Rosaci D, Sarn G, Garruzzo S (2009) MUADDIB: a distributed recommender system supporting device adaptivity. ACM Trans Inf Syst 27(4):24–65

    Article  Google Scholar 

  67. Rudametkin W, Touseau L, Perisanidi M, Gmez A, Donsez D NFCMuseum: an open-source middleware for augmenting museum exhibits. In: Proceedings of the international conference on pervasive services (ICPS), Sorrento, Italy

    Google Scholar 

  68. Saito K, Nakano R (1988) Medical diagnostic expert system based on PDP model. In: Proceedings of the IEEE international conference on neural networks, pp 255–262

    Google Scholar 

  69. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on the world wide web, Hong Kong, China, pp 285–295

    Google Scholar 

  70. Schafer JB, Konstan J, Riedl J (1999) Recommender systems in e-commerce. In: Proceedings of the 1st ACM conference on electronic commerce, pp 158–167

    Google Scholar 

  71. Shambour Q, Lu J (2010) A framework of hybrid recommendation system for government-to-business personalized e-services. In: Proceedings of the 7th international conference on information technology, pp 235–244

    Google Scholar 

  72. Sirin E, Parsia B, Wu D, Hendler JA, Nau DS (2004) HTN planning for web service composition using SHOP2. J Web Semant 1(4):377–396

    Article  Google Scholar 

  73. Smith D (1992) Expert systems for medical diagnosis: a study in technology transfer. J Tech Transf 17(4):45–53

    Article  Google Scholar 

  74. Smyth B, Cotter P (1999) Surfing the digital wave: generating personalized TV listings using collaborative, case-based recommendation. In: Proceedings of the 3rd international conference on case-based reasoning, Munich, Germany

    Google Scholar 

  75. Snell J, Tidwell D, Kulchenko P (eds) (2001) Programming web services with SOAP. O’Reilly Media, Sebastopol

    Google Scholar 

  76. Staab S, Studer R (eds) (2003) Handbook on ontologies. Springer, Berlin

    Google Scholar 

  77. Stanojevic M, Vranes S (2009) Semantic classifier for affective computing. In: Proceedings of the international conference on computational intelligence for modelling control & automation, Vienna, Austria, pp 849–854

    Google Scholar 

  78. Sørensen CF, Gimre S, Servold H, Brede S, Wang AI (2005) Development of location-aware applications. In: Mobile information systems II. Springer, Berlin, pp 171–186

    Google Scholar 

  79. Tkalcic M, Kosir A, Tasic J (2011) Affective recommender systems: the role of emotions in recommender systems. In: Proceedings of the 5th ACM conference on recommender systems

    Google Scholar 

  80. Tkalcic M, Kosir A, Tasic J (2011) Usage of affective computing in recommender systems. Elektrotech Vestn 78(1–2):12–17

    Google Scholar 

  81. TV-Anytime forum (2003) TV-Anytime specification series. ETSI standard TS 102 822

    Google Scholar 

  82. van Pinxteren Y, Geleijnse G, Kamsteeg P (2011) Deriving a recipe similarity measure for recommending healthful meals. In: Proceedings of the international conference on intelligent user interfaces (IUI), Palo Alto, CA

    Google Scholar 

  83. Wiesner M, Rotter S, Pfeifer D (2011) Leveraging semantic networks for personalized content in health recommender systems. In: Proceedings of the 24th international symposium on computer-based medical systems (CBMS), Bristol, UK

    Google Scholar 

  84. Yu C, Chang H (2009) Personalized location-based recommendation services for tour planning in mobile tourism applications. E-commerce and web technologies. Lect Notes Comput Sci 5692:38–49

    Article  Google Scholar 

  85. Yu Z, Zhou X, Hao Y, Gu J (2006) TV program recommendation for multiple viewers based on user profile merging. User Model User-Adapt Interact 16(1):63–82

    Article  Google Scholar 

  86. Yu Z, Zhou X, Yu Z, Zhang D, Chin CY (2006) An OSGi-based infrastructure for context-aware multimedia services. IEEE Commun Mag 44(10):136–142

    Article  Google Scholar 

  87. Zhou CL, Zhang ZF (2006) Progress and prospects of research on information processing techniques for intelligent diagnosis of traditional chinese medicine. J Chin Integr Med 4(6):560–566

    Article  Google Scholar 

  88. Zhou X, Xu Y, Li Y, Josang A, Cox C (2011) The state-of-the-art in personalized recommender systems for social networking. Artif Intell Rev 37(2):119–132

    Article  Google Scholar 

  89. Zimmerman J, Kurapati K, Buczak AL, Schaffer D, Gutta S, Martino J (2004) TV personalization system. Design of a TV show recommender engine and interface. In: Personalized digital television. Targeting programs to individual users. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

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Authors and Affiliations

  1. Department of Telematics Engineering, University of Vigo, EE Telecomunicación, Campus Universitario s/n, Vigo, 36310, Spain

    Martín López-Nores, Yolanda Blanco-Fernández, José J. Pazos-Arias & Manuela I. Martín-Vicente

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  1. Martín López-Nores
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  2. Yolanda Blanco-Fernández
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  3. José J. Pazos-Arias
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  4. Manuela I. Martín-Vicente
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Correspondence to Martín López-Nores .

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Editors and Affiliations

  1. Department of Languages and Computer Sys, Universidad Rey Juan Carlos, Tulipán s/n, Móstoles, 28933, Madrid, Spain

    Estefanía Martín

  2. IIC-Knowledge Engineering Institute, Universidad Autónoma de Madrid, Tomas y Valiente 11, Madrid, 28049, Madrid, Spain

    Pablo A. Haya

  3. Department of Computer Science and Engin, Universidad Autónoma de Madrid, Tomas y Valiente 11, Madrid, 28049, Madrid, Spain

    Rosa M. Carro

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López-Nores, M., Blanco-Fernández, Y., Pazos-Arias, J.J., Martín-Vicente, M.I. (2013). Context-Aware Recommender Systems Influenced by the Users’ Health-Related Data. In: Martín, E., Haya, P., Carro, R. (eds) User Modeling and Adaptation for Daily Routines. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-4471-4778-7_6

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