ARE RECOMMENDER SYSTEMS REAL-TIME IN MOBILE ENVIRONMENT? - Towards Instantaneous Recommenders

Armelle Brun, Anne Boyer

2010

Abstract

Recommendation technologies have traditionally been used in domains such as e-commerce to recommend resources to customers so as to help them to get the right resources at the right moment. The interest of modelbased collaborative filtering, as sequential association rules, in recommender systems has highly increased over the last few years. These models are usually presented as real-time recommenders. In the last few years, the m-commerce domain has emerged, that displays recommendations on the mobile device instead of the classical screen of the computer. In this paper user privacy preservation is an important objective and one way to be compliant with this constraint is to store the recommender on the mobile-side. Though model-based recommenders are real-time, many of them require a significant time to generate recommendations to users and may not be real-time anymore when implemented on a mobile device. Although some works focused on the way to decrease the time required to compute recommendations, the computation complexity still remains relatively high. We put forward a new incremental recommender to get instantaneous recommendations when exploiting usage mining recommender systems in the framework of m-commerce.

References

  1. Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art. IEEE transactions on knowledge and data engineering, 17(6):734-749.
  2. Bonnin, G., Brun, A., and Boyer, A. (2009). A loworder markov model integrating long-distance histories for collaborative recommender systems. In Proc. of the ACM Int. Conf. on Intelligent User Interfaces (IUI'09), pages 57-66, Sanibel Islands, USA.
  3. Bozdogan, H. (2004). Statistical Data Mining and Knowledge Discovery. Chapman & Hall/CRC.
  4. Brun, A. and Boyer, A. (2009). Towards privacy compliant and anytime recommender systems. In In Proceedings of the E-Commerce and Web Technologies Conference (EC-Web09), pages 276-287.
  5. Han, J. and Kamber, M. (2001). Data Mining: Concepts and Techniques. The M. Kaufmann Series in DMS.
  6. Hu, W., Yeh, J., and Lee, S. (2006). Adaptive web browsing using web mining technologies for internetenabled mobile handheld devices. In Proceedings of the 16th Information Resources Management Association (IRMA 2006) International Conference.
  7. Huang, Y., Kuo, Y., Chen, J., and Jeng, Y. (2006). Npminer: A real time recommendation algorithm by using web usage mining. Knowledge-Based Systems, 19:272-286.
  8. Lee, S. (2004). A mobile application of client side personalization based on wipi platform. In Proceedings of the Computational and Information Science Conference (CIS04), pages 903-909.
  9. Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2001). Effective personalization based on association rule discovery from web usage data. In in proceedings of the 3rd International Workshop on Web Information and Data Management, pages 9-15.
  10. Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2002). Using sequential and non-sequential patterns for predictive web usage mining tasks. In Proc. of the IEEE Int. Conf. on Data Mining (ICDM'2002).
  11. Nakagawa, M. and Mobasher, B. (2003). Impact of site characteristics on recommendation models based on association rules and sequential patterns. In Proceedings of the IJCAI'03 Workshop on Intelligent Techniques for Web Personalization.
  12. Srikant, R. and Agrawal, R. (1996). Mining sequential patterns: Generalizations and performance improvements. In Proc. of the 5th Int. Conf. on Extending Database Technology, pages 3-17.
  13. Tarasewich, P. (2003). Designing mobile commerce applications. Communications of the ACM, 46(12):57-60.
  14. Tveit, A. (2001). Peer-to-peer based recommendations for mobile commerce. In International Workshop on Mobile Commerce, Proceedings of the 1st international workshop on Mobile commerce, pages 26-29.
  15. Wan, Z. (2009). Personalized tourism information system in mobile commerce. In IEEE Int. Conf. on Management of e-Commerce and e-Government, pages 387-391.
  16. Yan, T., Jacobsen, M., Garcia-Molina, H., and Umeschwar, D. (1996). From user access patterns to dynamic hypertext linking. In Fifth Int. World Wide Web Conf.
  17. Zenebe, A., Ozok, A., and Norcio, A. (2005). Personalized recommender systems in e-commerce and mcommerce: A comparative study. In Proc. of the 11th Int. Conf. on Human-Computer Interaction.
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Paper Citation


in Harvard Style

Brun A. and Boyer A. (2010). ARE RECOMMENDER SYSTEMS REAL-TIME IN MOBILE ENVIRONMENT? - Towards Instantaneous Recommenders . In Proceedings of the 6th International Conference on Web Information Systems and Technology - Volume 1: WEBIST, ISBN 978-989-674-025-2, pages 101-106. DOI: 10.5220/0002807801010106


in Bibtex Style

@conference{webist10,
author={Armelle Brun and Anne Boyer},
title={ARE RECOMMENDER SYSTEMS REAL-TIME IN MOBILE ENVIRONMENT? - Towards Instantaneous Recommenders},
booktitle={Proceedings of the 6th International Conference on Web Information Systems and Technology - Volume 1: WEBIST,},
year={2010},
pages={101-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002807801010106},
isbn={978-989-674-025-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Web Information Systems and Technology - Volume 1: WEBIST,
TI - ARE RECOMMENDER SYSTEMS REAL-TIME IN MOBILE ENVIRONMENT? - Towards Instantaneous Recommenders
SN - 978-989-674-025-2
AU - Brun A.
AU - Boyer A.
PY - 2010
SP - 101
EP - 106
DO - 10.5220/0002807801010106