CFP last date
20 May 2024
Reseach Article

Properties of Context-Aware Recommender Systems: A Survey

by Fateme Keikha, Mahdi Heidari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 5
Year of Publication: 2015
Authors: Fateme Keikha, Mahdi Heidari
10.5120/ijca2015906379

Fateme Keikha, Mahdi Heidari . Properties of Context-Aware Recommender Systems: A Survey. International Journal of Computer Applications. 127, 5 ( October 2015), 9-13. DOI=10.5120/ijca2015906379

@article{ 10.5120/ijca2015906379,
author = { Fateme Keikha, Mahdi Heidari },
title = { Properties of Context-Aware Recommender Systems: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 5 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number5/22723-2015906379/ },
doi = { 10.5120/ijca2015906379 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:04.585027+05:30
%A Fateme Keikha
%A Mahdi Heidari
%T Properties of Context-Aware Recommender Systems: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 5
%P 9-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems provide personalized recommendation for their users. These systems are still needed to be optimized to provide more effective recommendations. In some models the context of user and the item is considered during the recommendation process so that it would be possible to make a better estimation of the user's rating. In this article context aware recommender models are addressed. Also the properties of each one of these systems are specified based on the general characteristics of the context aware recommender systems. Finally a general comparison of the level of utilization of these characteristics in the context aware models is done.

References
  1. M. Jamali and M. Ester, "Mining Social Networks for Recommendation," Tutorial of ICDM, vol. 11, 2011.
  2. G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions," Knowledge and Data Engineering, IEEE Transactions on, vol. 17, pp. 734-749, 2005.
  3. T. De Pessemier, T. Deryckere, and L. Martens, "Context aware recommendations for user-generated content on a social network site," in Proceedings of the seventh european conference on European interactive television conference, 2009, pp. 133-136.
  4. J. Gonzalo-Alonso, P. de Juan, E. Garcí-a-Hortelano, and C. Á. Iglesias, "A Hybrid Collaborative Filtering System for Contextual Recommendations in Social Networks," in Discovery Science, 2009, pp. 393-400.
  5. A. Akther, K. M. Alam, H.-N. Kim, and A. El Saddik, "Social network and user context assisted personalization for recommender systems," in Innovations in Information Technology (IIT), 2012 International Conference on, 2012, pp. 95-100.
  6. B. S. Sim, H. Kim, K. M. Kim, and H. Y. Youn, "Type-based context-aware service Recommender System for social network," in Computer, Information and Telecommunication Systems (CITS), 2012 International Conference on, 2012, pp. 1-5.
  7. P. J. Brown, J. D. Bovey, and X. Chen, "Context-aware applications: from the laboratory to the marketplace," Personal Communications, IEEE, vol. 4, pp. 58-64, 1997.
  8. G. Adomavicius and A. Tuzhilin, "Context-aware recommender systems," in Recommender systems handbook, ed: Springer, 2011, pp. 217-253.
  9. P. Kantor, F. Ricci, L. Rokach, and B. Shapira, "Recommender Systems Handbook: A complete guide for research scientists and practitioners," ed: Springer, 2010.
  10. B. N. Schilit and M. M. Theimer, "Disseminating active map information to mobile hosts," Network, IEEE, vol. 8, pp. 22-32, 1994.
  11. N. S. Ryan, J. Pascoe, and D. R. Morse, "Enhanced reality fieldwork: the context-aware archaeological assistant," in Computer applications in archaeology, 1998.
  12. A. K. Dey, G. D. Abowd, and D. Salber, "A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications," Human-computer interaction, vol. 16, pp. 97-166, 2001.
  13. J.-y. Hong, E.-h. Suh, and S.-J. Kim, "Context-aware systems: A literature review and classification," Expert Systems with Applications, vol. 36, pp. 8509-8522, 2009.
  14. G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin, "Incorporating contextual information in recommender systems using a multidimensional approach," ACM Transactions on Information Systems (TOIS), vol. 23, pp. 103-145, 2005.
  15. K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura, "Context-aware SVM for context-dependent information recommendation," in Mobile Data Management, 2006. MDM 2006. 7th International Conference on, 2006, pp. 109-109.
  16. C. Prahalad, "Beyond CRM: CK Prahalad predicts customer context is the next big thing," American Management Association MwWorld, 2004.
  17. C. Palmisano, A. Tuzhilin, and M. Gorgoglione, "Using context to improve predictive modeling of customers in personalization applications," Knowledge and Data Engineering, IEEE Transactions on, vol. 20, pp. 1535-1549, 2008.
  18. P. Dourish, "What we talk about when we talk about context," Personal and ubiquitous computing, vol. 8, pp. 19-30, 2004.
  19. J. L. Herlocker and J. A. Konstan, "Content-independent task-focused recommendation," Internet Computing, IEEE, vol. 5, pp. 40-47, 2001.
  20. H. Ahn, K.-j. Kim, and I. Han, "Mobile advertisement recommender system using collaborative filtering: Mar-cf," in Proceedings of the 2006 conference of the Korea society of management information systems, 2006.
  21. S. Lombardi, S. S. Anand, and M. Gorgoglione, "Context and customer behaviour in recommendation," 2009.
  22. L. Baltrunas and F. Ricci, "Context-dependent items generation in collaborative filtering," in Proceedings of the 2009 Workshop on Context-Aware Recommender Systems, 2009, pp. 22-25.
  23. L. Baltrunas and X. Amatriain, "Towards time-dependant recommendation based on implicit feedback," in Workshop on context-aware recommender systems (CARS’09), 2009.
  24. M. Kahng, S. Lee, and S.-g. Lee, "Ranking in context-aware recommender systems," in Proceedings of the 20th international conference companion on World wide web, 2011, pp. 65-66.
  25. L. Baltrunas, B. Ludwig, and F. Ricci, "Matrix factorization techniques for context aware recommendation," in Proceedings of the fifth ACM conference on Recommender systems, 2011, pp. 301-304.
  26. D. Shin, J.-w. Lee, J. Yeon, and S.-g. Lee, "Context-aware recommendation by aggregating user context," in Commerce and Enterprise Computing, 2009. CEC'09. IEEE Conference on, 2009, pp. 423-430.
  27. T. Bogers, "Movie recommendation using random walks over the contextual graph," in Proc. of the 2nd Intl. Workshop on Context-Aware Recommender Systems, 2010.
  28. A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, "Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering," in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 79-86.
  29. Z. Gantner, S. Rendle, and L. Schmidt-Thieme, "Factorization models for context-/time-aware movie recommendations," in Proceedings of the Workshop on Context-Aware Movie Recommendation, 2010, pp. 14-19.
  30. J. Hong, E.-H. Suh, J. Kim, and S. Kim, "Context-aware system for proactive personalized service based on context history," Expert Systems with Applications, vol. 36, pp. 7448-7457, 2009.
  31. Y. Shi, M. Larson, and A. Hanjalic, "Mining contextual movie similarity with matrix factorization for context-aware recommendation," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, p. 16, 2013.
  32. J.-H. Kim, D. Lee, and K.-Y. Chung, "Context-aware based item recommendation for personalized service," in Information Science and Applications (ICISA), 2011 International Conference on, 2011, pp. 1-6.
  33. Z. Huang, X. Lu, and H. Duan, "Context-aware recommendation using rough set model and collaborative filtering," Artificial Intelligence Review, vol. 35, pp. 85-99, 2011.
  34. F. Keikha, M. Fathian, and M. R. Gholamian, "TB-CA: A hybrid method based on trust and context-aware for recommender system in social networks Pages 471-480 Right click to download the paper Download PDF."
Index Terms

Computer Science
Information Sciences

Keywords

Recommendation system Social network Context awareness