Skip to main content

Towards a Context-Aware Mobile Recommendation Architecture

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8640))

Abstract

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. On the one hand, it is necessary to identify which items are relevant for the user at a particular moment and place. On the other hand, some mechanism would be needed to rank the different alternatives. Recommendation systems, that offer relevant items to the users, have been proposed as a solution to these problems. However, they usually target very specific use cases (e.g., books, movies, music, etc.) and are not designed with mobile users in mind, where the context and the movements of the users may be important factors to consider when deciding which items should be recommended.

In this paper, we present a context-aware mobile recommendation architecture specifically designed to be used in mobile computing environments. The interest of context-aware recommendation systems has been already shown for certain application domains, indicating that they lead to a performance improvement over traditional recommenders. However, only very few studies have provided insights towards the development of a generic architecture that is able to exploit static and dynamic context information in mobile environments. We attempt to make a step in that direction and encourage further research in this area.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23(1), 103–145 (2005)

    Article  Google Scholar 

  2. Adomavicius, G., Jannach, D.: Preface to the special issue on context-aware recommender systems. User Modeling and User-Adapted Interaction 24(1-2), 1–5 (2014)

    Article  Google Scholar 

  3. Panniello, U., Tuzhilin, A., Gorgoglione, M.: Comparing context-aware recommender systems in terms of accuracy and diversity: Which contextual modeling, pre-filtering and post-filtering methods perform the best. User Modeling and User-Adapted Interaction 24(1-2), 35–65 (2014)

    Article  Google Scholar 

  4. Hussein, T., Linder, T., Gaulke, W., Ziegler, J.: Hybreed: A software framework for developing context-aware hybrid recommender systems. User Modeling and User-Adapted Interaction 24(1-2), 121–174 (2014)

    Article  Google Scholar 

  5. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)

    Article  Google Scholar 

  6. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: ACM Conference on Recommender Systems (RecSys 2008), pp. 335–336. ACM (2008)

    Google Scholar 

  7. Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Recommender Systems Handbook, pp. 217–253. Springer (2011)

    Google Scholar 

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

    Article  Google Scholar 

  9. Santos, O., Boticario, J.: Modeling recommendations for the educational domain. Procedia Computer Science 1(2), 2793–2800 (2010)

    Article  Google Scholar 

  10. Sielis, G., Mettouris, C., Papadopoulos, G., Tzanavari, A., Dols, R., Siebers, Q.: A context aware recommender system for creativity support tools. Journal of Universal Computer Science 17(12), 1743–1763 (2011)

    Google Scholar 

  11. Mettouris, C., Papadopoulos, G.: Contextual modelling in context-aware recommender systems: A generic approach. In: Haller, A., Huang, G., Huang, Z., Paik, H.-y., Sheng, Q.Z. (eds.) WISE 2011 and 2012. LNCS, vol. 7652, pp. 41–52. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. Loizou, A., Dasmahapatra, S.: Recommender systems for the Semantic Web. In: ECAI 2006 Recommender Systems Workshop (2006)

    Google Scholar 

  13. Woerndl, W., Huebner, J., Bader, R., Gallego-Vico, D.: A model for proactivity in mobile, context-aware recommender systems. In: Fifth ACM Conference on Recommender Systems (RecSys 2011), pp. 273–276. ACM (2011)

    Google Scholar 

  14. Mettouris, C., Papadopoulos, G.: Ubiquitous recommender systems. Computing 96(3), 223–257 (2014)

    Article  Google Scholar 

  15. Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)

    Google Scholar 

  16. Panniello, U., Tuzhilin, A., Gorgoglione, M., Palmisano, C., Pedone, A.: Experimental comparison of pre- versus post-filtering approaches in context-aware recommender systems. In: Third ACM Conference on Recommender Systems (RecSys 2009), pp. 265–268. ACM (2009)

    Google Scholar 

  17. Gorgoglione, M., Panniello, U., Tuzhilin, A.: The effect of context-aware recommendations on customer purchasing behavior and trust. In: Fifth ACM Conference on Recommender Systems (RecSys 2011), pp. 85–92. ACM (2011)

    Google Scholar 

  18. Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Second ACM International Conference on Web Search and Data Mining (WSDM 2009), pp. 5–14. ACM (2009)

    Google Scholar 

  19. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowledge-Based Systems 46, 109–132 (2013)

    Article  Google Scholar 

  20. Chen, G., Kotz, D.: A survey of context-aware mobile computing research. Technical Report TR2000-381, Dartmouth College, Computer Science, Hanover, NH, USA (2000)

    Google Scholar 

  21. Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing 2(4), 263–277 (2007)

    Article  Google Scholar 

  22. Mascolo, C., Capra, L., Emmerich, W.: Mobile computing middleware. In: Gregori, E., Anastasi, G., Basagni, S. (eds.) NETWORKING 2002. LNCS, vol. 2497, pp. 20–58. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  23. Luo, Y., Wolfson, O.: Mobile P2P databases. In: Encyclopedia of GIS, pp. 671–677. Springer (2008)

    Google Scholar 

  24. Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  25. Avesani, P., Massa, P., Tiella, R.: A trust-enhanced recommender system application: Moleskiing. In: ACM Symposium on Applied Computing (SAC 2005), pp. 1589–1593. ACM (2005)

    Google Scholar 

  26. Liu, B.: Sentiment analysis and subjectivity. CRC Press, Taylor and Francis Group, Boca Raton, FL (2010)

    Google Scholar 

  27. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience (2000)

    Google Scholar 

  28. Vapnik, V., Cortes, C.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)

    MATH  Google Scholar 

  29. Kramer, S., Widmer, G., Pfahringer, B., Groeve, M.D.: Prediction of ordinal classes using regression trees. Foundations of Intelligent Systems 47(1-2), 1–13 (2001)

    MATH  Google Scholar 

  30. Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology 7(4), 23:1–23:41 (2007)

    Google Scholar 

  31. Ilarri, S., Mena, E., Illarramendi, A.: Location-dependent query processing: Where we are and where we are heading. ACM Computing Surveys 42(3), 12:1–12:73 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

del Carmen Rodríguez-Hernández, M., Ilarri, S. (2014). Towards a Context-Aware Mobile Recommendation Architecture. In: Awan, I., Younas, M., Franch, X., Quer, C. (eds) Mobile Web Information Systems. MobiWIS 2014. Lecture Notes in Computer Science, vol 8640. Springer, Cham. https://doi.org/10.1007/978-3-319-10359-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10359-4_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10358-7

  • Online ISBN: 978-3-319-10359-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics