Skip to main content
SpringerLink
Log in

Ubiquitous recommender systems

  • Published:
Computing Aims and scope Submit manuscript

Abstract

Ubiquitous recommender systems combine characteristics from ubiquitous systems and recommender systems in order to provide personalized recommendations to users in ubiquitous environments. Although not a new research area, ubiquitous recommender systems research has not yet been reviewed and classified in terms of ubiquitous research and recommender systems research, in order to deeply comprehend its nature, characteristics, relevant issues and challenges. It is our belief that ubiquitous recommenders can nowadays take advantage of the progress mobile phone technology has made in identifying items around, as well as utilize the faster wireless connections and the endless capabilities of modern mobile devices in order to provide users with more personalized and context-aware recommendations on location to aid them with their task at hand. This work focuses on ubiquitous recommender systems, while a brief analysis of the two fundamental areas from which they emerged, ubiquitous computing and recommender systems research is also conducted. Related work is provided, followed by a classification schema and a discussion about the correlation of ubiquitous recommenders with classic ubiquitous systems and recommender systems: similarities inevitably exist, however their fundamental differences are crucial. The paper concludes by proposing UbiCARS: a new class of ubiquitous recommender systems that will combine characteristics from ubiquitous systems and context-aware recommender systems in order to utilize multidimensional context modeling techniques not previously met in ubiquitous recommender systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. A proactive system acts considering and anticipating any problematic situations and events that could happen in the future.

  2. The author uses the term “resource” for the object to be annotated and “information” for the annotation itself. The present work uses the term “information resource” to refer to the annotation and no special term for the object to be annotated.

  3. We will refer to the user who is to be provided with recommendations as the “active user”.

  4. Things in the proximity could be other people, objects, or places.

References

  1. Adomavicius G, Sankaranarayanan R, Sen S, Tuzhilin A (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst (TOIS) 23:103–145

    Article  Google Scholar 

  2. Adomavicius G, Tuzhilin A (2008) Context-aware recommender systems. In: Proc. RecSys 2008, the 2008 ACM conference on recommender systems, pp 335–336

  3. Baltrunas L, Amatriain X (2009) Towards time-dependant recommendation based on implicit feedback, workshop on context-aware recommender systems, CARS 2009. In: ACM RecSys, vol 2009

  4. Baltrunas L, Ricci F (2009) Context-dependent items generation in collaborative filtering, workshop on context-aware recommender systems, CARS 2009. In: ACM RecSys, vol 2009

  5. Bilandzic M, Foth M, Luca AD (2008) CityFlocks: designing social navigation for urban mobile information systems. In: Proc. of the 7th ACM conference on designing interactive systems. ACM, Cape Town, pp 174–183

  6. Bogers T (2010) Movie Recommendation using random walks over the contextual graph. In: Proc. CARS 2010, the 2nd workshop on context-aware recommender systems

  7. Böhmer M, Bauer G, Krüger A (2010) Exploring the design space of context-aware recommender systems that suggest mobile applications. In: Proc. CARS 2010, the 2nd workshop on context-aware recommender systems

  8. Bourke S, McCarthy K, Smyth B (2010) The social camera: recommending photo composition using contextual features. In: Proc. CARS 2010, the 2nd workshop on context-aware recommender systems

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

    Article  MATH  Google Scholar 

  10. Burrell J, Gay GK (2001) Collectively defining context in a mobile, networked computing environment, CHI ’01 extended abstracts on human factors in computing systems. ACM, Seattle

    Google Scholar 

  11. Burrell J, Gay GK (2002) E-Graffiti: evaluating real-world use of a context-aware system, interacting with computers

  12. Cantador I, Castells P (2009) Semantic contextualisation in a news recommender system, workshop on context-aware recommender systems, CARS 2009. In: ACM RecSys, vol 2009

  13. Carolis BD, Mazzotta I, Novielli N, Silvestri V (2009) Using common sense in providing personalized recommendations in the tourism domain, workshop on context-aware recommender systems, CARS 2009. In: ACM RecSys, vol 2009

  14. Cena F, Console L, Gena C, Goy A, Levi G, Modeo S, Torre I (2006) Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide. AI Commun 19:369–384

    MATH  MathSciNet  Google Scholar 

  15. Davies N, Gellersen H (2002) Beyond prototypes: challenges in deploying ubiquitous systems. IEEE Pervasive Comput 1:26–35

    Article  Google Scholar 

  16. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst 22:143–177

    Article  Google Scholar 

  17. Dey AK, Abowd GD, Abowd D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications, Hum Comput Interact 16:97–166

    Article  Google Scholar 

  18. Domingues MA, Jorge AM, Soares C (2009) Using contextual information as virtual items on top-N recommender systems, workshop on context-aware recommender systems, CARS 2009. In: ACM RecSys, vol 2009

  19. Domnitcheva S (2001) Location modeling: state of the art and challenges. In: Proc. the workshop on location modeling for ubiquitous computing, pp 13–19

  20. Hansen FA (2006) Ubiquitous annotation systems: technologies and challenges. In: Proc. ACM, the seventeenth conference on hypertext and hypermedia, pp 121–132

  21. Henricksen K, Indulska J, Rakotonirainy A (2001) Infrastructure for pervasive computing: challenges. In: Proc. workshop on pervasive computing informatik, vol 01, pp 214–222

  22. Hinze A, Buchanan G (2005) Context-awareness in mobile tourist information systems: challenges for user interaction. In: Proc. international workshop on context in mobile HCI at the conference for 7th international conference on human computer interaction with mobile devices and services

  23. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, Cambridge, pp 289–298

  24. Karypis G (2000) Evaluation of item-based top-N recommendation algorithms. In: Proc. the 10th international conference on information and, knowledge management, pp 247–254

  25. Kjeldskov J, Skov MB (2007) Exploring context-awareness for ubiquitous computing in the healthcare domain. Person Ubiquitous Comput 11:549–562

    Article  Google Scholar 

  26. Kourouthanassis P, Spinellis D, Roussos G, Giaglis GM (2002) Intelligent cokes and diapers: Mygrocer ubiquitous computing environment. In: Proc. the 1st international mobile business conference

  27. Lenders V, Koukoumidis E, Zhang P, Martonosi M (2008) Location-based trust for mobile user-generated content: applications, challenges and implementations. In: Proc. the 9th workshop on mobile computing systems and applications, ACM, pp 60–64

  28. Loizou A, Dasmahapatra S (2006) Recommender systems for the semantic web. In: ECAI 2006 recommender systems workshop

  29. Lombardi S, Anand S, Gorgoglione M (2009) Context and customer behavior in recommendation, workshop on context-aware recommender systems, CARS 2009. In: ACM RecSys, vol 2009

  30. Mancini C, Thomas K, Rogers Y, Price BA, Jedrzejczyk L, Bandara AK, Joinson AN, Nuseibeh B (2009) From spaces to places: emerging contexts in mobile privacy. In: Proceedings of the 11th international conference on ubiquitous computing, New York, vol 2009, pp 1–10

  31. McDonald DW (2003) Ubiquitous recommendation systems, computer, vol 36, no 10, p 111

  32. Miller BN, Albert I, Lam SK, Konstan JA, Riedl J (2003) MovieLens unplugged: experiences with a recommender system on four mobile devices. In: Proc. the 8th international conference on intelligent user interfaces

  33. Oku K, Nakajima S, Miyazaki J, Uemura S (2006) Context-aware SVM for context-dependent information recommendation. In: IEEE international conference on mobile data management, p 109

  34. Oku K, Nakajima S, Miyazaki J, Uemura S, Kato H, Hattori F (2010) A recommendation system considering users’ past/current/future contexts. In: Proc. CARS 2010, the 2nd workshop on context-aware recommender systems

  35. Persson P, Espinoza F, Fagerberg P, Sandin A, Cöster R (2002) GeoNotes: a location-based information system for public spaces. In: Hook K, Benyon D, Munro A (eds) Readings in social navigation of information space, pp 151–173

  36. Reischach FV, Guinard D, Michahelles F, Fleisch E (2009) A mobile product recommendation system interacting with tagged products. In: Proc. the 2009 IEEE international conference on pervasive computing and, communications, pp 1–6

  37. Reischach FV, Michahelles F, Schmidt A (2009) The design space of ubiquitous product recommendation systems. In: Proc. the 8th international conference on mobile and ubiquitous multimedia, pp 1–10

  38. Reischach FV, Michahelles F (2008) Apriori: a ubiquitous product rating system. In: PERMID ’08, workshop on pervasive mobile interaction devices at pervasive conference

  39. Resatsch F, Karpischek S, Sandner U, Hamacher S (2007) Mobile sales assistant: NFC for retailers. In: Proc. mobile HCI ’07, the 9th international conference on human computer interaction with mobile devices and services

  40. Resatsch F, Sandner U, Leimeister JM, Krcmar H (2008) Do point of sale RFID-based information services make a difference? Analyzing consumer perceptions for designing smart product information services in retail business. Electron Markets 18(3):216–231

    Google Scholar 

  41. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for e-commerce. In: Proc. the 2nd ACM conference on electronic commerce, pp 158–167

  42. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proc. the 10th international conference on World Wide Web, pp 285–295

  43. Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Person Commun 8:10–17

    Article  Google Scholar 

  44. Schilit BN, Lamarca A, Borriello G, Griswold WG, Mcdonald D, Lazowska E, Hong J, Iverson V (2003) Challenge: ubiquitous location-aware computing and the Place Lab initiative. In: Proc. WMASH ’03, the 1st ACM international workshop on wireless mobile applications and services on WLAN hotspots, pp 29–35

  45. Setten MV, Pokraev S, Koolwaaij J (2004) Context-aware recommendations in the mobile tourist application compass. In: Nejdl W, De Bra P (eds) Adaptive hypermedia, pp 235–244

  46. Streefkerk JW, Esch-Bussemakers MPV, Neerincx MA (2008) Field evaluation of a mobile location-based notification system for police officers. In: Proc. the 10th international conference on Human computer interaction with mobile devices and services, ACM, pp 101–108

  47. Takeuchi Y, Sugimoto M (2007) A user-adaptive city guide system with an unobtrusive navigation interface. Person Ubiquitous Comput 13(2):119–132

    Article  Google Scholar 

  48. Tungare M, Burbey I, Pérez-Quiñones MA (2006) Evaluation of a location-linked notes system. In: Proc. the 44th annual Southeast regional conference, ACM, pp 494–499

  49. Waller V, Johnston RB (2009) Making ubiquitous computing available. Commun ACM 52:127–130

    Article  Google Scholar 

  50. Want R, Pering T (2005) System challenges for ubiquitous and pervasive computing. In: Proc. the 27th international conference on Software engineering, ACM, pp 9–14

  51. Weiser M (1991) The computer for the 21st century. Scientific American

  52. Yu Z, Zhou X, Zhang D, Chin C, Wang X, Men J (2006) Supporting context-aware media recommendations for smart phones. IEEE Pervasive Comput 5(3):68–75

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, 1 University Avenue, P.O. Box 20537, 2109 , Nicosia, Cyprus

    Christos Mettouris & George A. Papadopoulos

Authors
  1. Christos Mettouris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George A. Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Mettouris.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mettouris, C., Papadopoulos, G.A. Ubiquitous recommender systems. Computing 96, 223–257 (2014). https://doi.org/10.1007/s00607-013-0351-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-013-0351-z

Keywords

Mathematics Subject Classification

Search

Navigation