A Survey on Recommendation System for Big Data Applications
Abstract
Recommender systems is very useful tools for providing proper suggestions to users before buying any product online. Presently a days, the measure of users, services and online data has expands rapidly, accepting the enormous information investigation issue for recommender system. As a result, existing recommender systems have scalability and inefficiency issue when processing or analyzing extensive data, due to this distributed system come into existence. In this paper, explain the recommendation system related research and introduces different techniques used by the recommender system. Finally we will give details about the main challenges recommender systems arrive across.
Keywords
Full Text:
PDFReferences
Shunmei Meng, Wanchun Dou, Xuyun Zhang, Jinjun Chen, “KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications,” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, TPDS-12-1141-2013.
C. Lynch, “Big Data: How do your data grow?,” Nature, Vol. 455, No. 7209, pp. 28-29, 2008.
G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, IEEE Internet Computing, Vol. 7, No.1, pp. 76-80, 2003.
M. Bjelica, “Towards TV Recommender System Experiments with User Modeling,” IEEE Transactions on Consumer Electronics, Vol. 56, No.3, pp. 1763-1769, 2010.
Swati Pandey, T. Senthil Kumar, “COSTOMIZATION OF RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING ALGORITHM ON CLOUD USING MAHOUT,” International Journal of Research in Engineering and Technology, Volume: 03, Special Issue: 07, May-2014.
Mukta kohar, Chhavi Rana, “Survey Paper on Recommendation System,” International Journal of Computer Science and Information Technologies, Vol. 3 (2), 3460-3462, 2012.
R. Burke, “Hybrid Recommender Systems: Survey and Experiments,” User Modeling and User-Adapted Interaction, Vol. 12, No.4, pp. 331-370, 2002.
Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor, “Recommender Systems Handbook”.
Zhi Dan Zhao and Ming Sheng Shang, “User based collaborative filtering recommendation algorithm an hadoop,” Third International Conference on Knowledge Discovery and Data Mining, 2010.
Manos Papagelis and Dimitris Plexousakis, “Qualitative analysis of user based and item based prediction algorithms for recommendation agents,” Engineering Applications of Artificial Intelligence, 18 781–789, 2005.
Xiao Yan Shi, Hong Wu Ye, Song Jie Gong, “A personalized recommender integrating item based and user based collaborative filtering,” IEEE 2008.
Yanhong Guo, Xuefen Cheng, Dahai Dong, Chunyu Luo,Rishuang Wang, “An improved collaborative filtering algorithm based on trust in e-commerce recommendation system,” IEEE 2010.
Swati Pandey, Dr. T. Senthil Kumar, “A Survey on Recommendation Algorithm for Movie Recommendation on Cloud,” Proc. of the Intl. Conf. on Advances In Engineering And Technology - ICAET-2014.
Lalita Sharma, Anju Gera, “A Survey of Recommendation System: Research Challenges,” International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue5- May 2013.
Ala S. Alluhaidan, “Recommender System Using Collaborative Filtering Algorithm”.
M. Alduan, F. Alvarez, J. Menendez, and O. Baez, “Recommender System for Sport Videos Based on User Audiovisual Consumption,” IEEE Transactions on Multimedia, Vol. 14, No.6, pp. 1546-1557, 2013.
Z. Zheng, X Wu, Y Zhang, M Lyu and J Wang, “QoS Ranking Prediction for Cloud Services,” IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 6, pp. 1213-1222, 2013.
G. Adomavicius, and Y. Kwon, “New Recommendation Techniques for Multicriteria Rating Systems,” IEEE Intelligent Systems, Vol. 22, No. 3, pp. 48-55, 2007.
http://en.wikipedia.org/wiki/Recommender_system.
J.Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen, “Collaborative Filtering Recommender Systems,” The Adaptive Web, LNCS 4321, pp. 291 – 324, 2007.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.