Following the user's interests in mobile context-aware recommender systems: The hybrid-e-greedy algorithm

Proceedings - 26th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2012

Volumn , Issue , 2012, Pages 657-662

  Bouneffouf, Djallel ^a   Bouzeghoub, Amel ^a   Gançarski, Alda Lopes ^a  

^a INSTITUT TELECOM   (France)

Author keywords

context aware recommender systems; recommandation; situation

Indexed keywords

CONTENT BASED FILTERING; CONTEXT-AWARE RECOMMENDER SYSTEMS; GREEDY ALGORITHMS; MOBILE APPLICATIONS; RECOMMANDATION; SITUATION; USER INFORMATION; USER'S INTEREST;

ALGORITHMS;

RECOMMENDER SYSTEMS;

EID: 84860738619     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WAINA.2012.200     Document Type: Conference Paper

References (18)

1. Alexandre S, Moira C. Norrie, Michael Grossniklaus and Beat Signer, "Spatio-Temporal Proximity as a Basis for Collaborative Filtering in Mobile Environments".Workshop on Ubiquitous Mobile Information and Collaboration Systems, CAiSE, 2006.
2. Bellotti, V., Begole, B., Chi, E.H., Ducheneaut, N., Fang, J., Isaacs, E. Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide. Proceedings On the Move, 2008.
3. Bettini, C., et al., A survey of context modelling and reasoning techniques, Pervasive and Mobile Computing, 2009.
4. Dey, A. K. Providing Architectural Support for Building Context- Aware Applications. Ph.D. thesis, College of Computing, Georgia Institute of Technology, 2000.
5. Dobson, S. Leveraging the subtleties of location, in Proc. of Smart Objects and Ambient Intelligence, 2005.
6. Lakshmish R, Deepak P, Ramana P, Kutila G, Dinesh Garg, Karthik V, Shivkumar K. Tenth International Conference on Mobile Data Management: Systems, Services and Middleware CAESAR: A Mobile context-aware, Social Recommender System for Low-End Mobile Devices, 2009.
7. Lihong Li, Presented at the Nineteenth International Conference on World Wide Web, Raleigh, NC, USA, 2010 A Contextual-Bandit Approach to Personalized News Article Recommendation, 2010.
8. Mieczysław A. Kłopotek. Approaches to Cold-Start in recommender systems. studia informatica Systems and information technology, 2009.
9. Ourdia, B.,Tamine, L., Boughanem M. Dynamically Personalizing Search Results for Mobile Users: FQAS'09, pp. 99-110. In Proceedings of the 8th International Conference on Flexible Query Answering Systems (2009).
10. Panayiotou, C., Samaras, G. Mobile User Personalization with Dynamic Profiles. On the Move to Meaningful Internet Systems, 2006.
11. Pan, F. Representing complex temporal phenomena for the semantic web and natural language. Ph.D thesis, University of Southern California, Dec, 2007.
12. Samaras, G., Panayiotou, C. Personalized portals for the wireless user based on mobile agents. Proc. 2nd Int'l workshop on Mobile Commerce, pp. 70-74, 2002.
13. Sparck-Jones, K. Index Term Weighting, Information Storage and Retrieval, vol. 9, no. 2011, pp. 619-633, 1973.
14. Taylor E and PeterStone. Transfer learning for bandit algorithm domains: A survey. Journal of Machine Learning Research, 10(1):1633-1685, 2009.
15. Varma, V., Sriharsha, N., Pingali, P. Personalized web search engine for mobile devices, Int'l Workshop on Intelligent Information Access, IIIA'06, 2006.
16. Wei Li ,Exploitation and Exploration in a Performance based Contextual Advertising SystemProceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data miningACM New York, NY, USA, 2010.
17. Wu, Z., Palmer, M. Verb Semantics and Lexical Selection. In Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, 1994.
18. Zhang T. and Iyengar, V. "Recommender systems using linear classifiers," The Journal of Machine Learning Research, vol. 2, p. 334, 2002.