Social Voting Techniques: A Comparison of the Methods Used for Explicit Feedback in Recommendation Systems
DOI:
https://doi.org/10.9781/ijimai.2011.1410Keywords:
Recommendation Systems, Method, Feedback, RatingAbstract
Web recommendation systems usually brings a content list to users based on previous ratings made by them to other similar contents through some social voting mean. This paper aims to present a comparison of the main explicit rating methods used by web recommendation systems. The goal of this survey is to determine which of the studied methods fits better to user preferences when they rate a content on the web; based on the obtained results, a recommendation system can be implemented using an explicit feedback method to achieve this goal.Downloads
References
[1] O. Sanjuan Martínez, Pelayo G-Bustelo,C., González Crespo, R., Torres Franco, E., "Using Recommendation System for E-learning Environments at degreelevel," International Journal of Artificial Intelligence and Interactive Multimedia, vol. 1, pp. 67-70, 2009.
[2] J. O'Donovan and B. Smyth, "Trust in recommender systems," presented at the Proceedings of the 10th international conference on Intelligent user interfaces, San Diego, California, USA, 2005.
[3] N. Taghipour and A. Kardan, "A hybrid web recommender system based on Q-learning," presented at the Proceedings of the 2008 ACM symposium on Applied computing, Fortaleza, Ceara, Brazil, 2008.
[4] S. Noor and K. Martinez, "Using social data as context for making recommendations: an ontology based approach," presented at theProceedings of the 1st Workshop on Context, Information and Ontologies, Heraklion, Greece, 2009.
[5] P. Wang, "Why recommendation is special?," Workshop on Recommender Systems, part of the 15th National Conference on Artificial Intelligence, pp. 111-113, 1998.
[6] R. González Crespo, et al., "Recommendation System based on user interaction data applied to intelligent electronic books," Comput. Hum. Behav., vol. 27, pp. 1445-1449, 2011.
[7] P. Resnick and H. R. Varian, "Recommender systems," Commun. ACM, vol. 40, pp. 56-58, 1997.
[8] G. Adomavicius, et al., "Incorporating contextual information in recommender systems using a multidimensional approach," ACM Trans. Inf. Syst., vol. 23, pp. 103-145, 2005.
[9] C.-N. Ziegler, et al., "Improving recommendation lists through topic diversification," presented at the Proceedings of the 14th international conference on World Wide Web, Chiba, Japan, 2005.
[10] D. Kelly and J. Teevan, "Implicit feedback for inferring user preference: a bibliography," SIGIR Forum, vol. 37, pp. 18-28, 2003.
[11] Y. Hu, et al., "Collaborative Filtering for Implicit Feedback Datasets," presented at the Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, 2008.
[12] G. Jawaheer, et al., "Comparison of implicit and explicit feedback from an online music recommendation service," presented at the Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, Barcelona, Spain, 2010.
[13] M. Claypool, et al., "Inferring User Interest," IEEE Internet Computing, vol. 5, pp. 32-39, 2001.
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