Hike the performance of collaborative filtering algorithm with the inclusion of multiple attributes
Автор: Barkha A. Wadhvani, Sameer A. Chauhan
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 4 Vol. 10, 2018 года.
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At a recent time, digital data increases very speedily from small business to large business. In this span of internet explosion, choices are also increases and it makes the selection of products very difficult for users so it demands some recommendation system which provides good and meaningful suggestions to users to help them to purchase or select products of their own choice and get benefited. Collaborative filtering technique works very productive to provide personalized suggestions. It works based on the past given ratings, behavior and choices of users to provide recommendations. To boost its performance many other algorithms and techniques can be combined with it. This paper describes the method to boost the performance of collaborative filtering algorithm by taking multiple attributes in consideration where each attribute has some weight.
Recommendation system, Collaborative filtering algorithm, Multiple attributes, Distributed computation, Compute intensive tasks, TOPSIS, Hadoop
Короткий адрес: https://sciup.org/15016256
IDR: 15016256 | DOI: 10.5815/ijitcs.2018.04.08
Список литературы Hike the performance of collaborative filtering algorithm with the inclusion of multiple attributes
- Xiaoyuan Su and Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, vol. 2009, Article ID 421425, 19 pages, 2009.
- Rasim M. Alguliyev, Rena T. Gasimova, Rahim N. Abbaslı,"The Obstacles in Big Data Process", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.31-38, 2017. DOI: 10.5815/ijitcs.2017.04.05.
- Pooja Chaudhary and Virendra Kumar Yadav. A Survey on Security Issues and the Existing Solutions in Big Data. International Journal of Computer Applications 162(1):33-37, March 2017.
- I. Gorton, P. Greenfield, A. Szalay and R. Williams, "Data-Intensive Computing in the 21st Century," in Computer, vol. 41, no. 4, pp. 30-32, April 2008. doi: 10.1109/MC.2008.122.
- Weiyan X., Wenqing H., Dong L., Youyi D. (2013) A Distributed Computing Platform for Task Stream Processing. In: Yang Y., Ma M., Liu B. (eds) Information Computing and Applications. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg.
- Barkha A. Wadhvani, Sameer A. Chauhan, "A Review on Scale up the Performance of Collaborative Filtering Algorithm", International Journal of Science and Research (IJSR), https://www.ijsr.net/archive/v6i4/v6i4.php, Volume 6 Issue 4, April 2017, 2528 - 2533, DOI: 10.21275/ART20172947
- Bibhudutta Jena, Mahendra Kumar Gourisaria, Siddharth Swarup Rautaray, Manjusha Pandey,"A Survey Work on Optimization Techniques Utilizing Map-Reduce Framework in Hadoop Cluster", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.4, pp.61-68, 2017. DOI: 10.5815/ijisa.2017.04.07.
- Luis Emilio Alvarez-Dionisi,"Envisioning Skills for Adopting, Managing, and Implementing Big Data Technology in the 21st Century", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.1, pp.18-25, 2017. DOI: 10.5815/ijitcs.2017.01.03.
- Konstantin Shvachko, Hairong Kuang, Sanjay Radia, and Robert Chansler. 2010. The Hadoop Distributed File System. In Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) (MSST '10). IEEE Computer Society, Washington, DC, USA, 1-10.
- Seema Maitrey, C.K. Jha, MapReduce: Simplified Data Analysis of Big Data, In Procedia Computer Science, Volume 57, 2015, Pages 563-571, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2015.07.392.
- Z. Ma, Y. Yang, F. Wang, C. Li and L. Li, "The SOM Based Improved K-Means Clustering Collaborative Filtering Algorithm in TV Recommendation System," 2014 Second International Conference on Advanced Cloud and Big Data, Huangshan, 2014, pp.
- R. Hu, W. Dou and J. Liu, "ClubCF: A Clustering-Based Collaborative Filtering Approach for Big Data Application," in IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 3, pp. 302-313, Sept. 2014.
- P. Yu, "Recommendation method for mobile network based on user characteristics and user trust relationship," 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, 2016, pp. 1-6.
- Z. Gao, Z. Lu, N. Deng and K. Niu, "A novel collaborative filtering recommendation algorithm based on user location," 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Nantou, 2016, pp. 1-2.
- D. Lalwani, D. V. L. N. Somayajulu and P. R. Krishna, "A community driven social recommendation system," 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015, pp. 821-826.
- P. Ghuli, A. Ghosh and R. Shettar, "A collaborative filtering recommendation engine in a distributed environment," 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, 2014, pp. 568-574.
- Z. L. Zhao, C. D. Wang, Y. Y. Wan, Z. W. Huang and J. H. Lai, "Pipeline Item-Based Collaborative Filtering Based on MapReduce," 2015 IEEE Fifth International Conference on Big Data and Cloud Computing, Dalian, 2015, pp. 9-14.
- W. Zhoul and W. Jiang, "Two-phase TOPSIS of uncertain multi-attribute group decision-making," in Journal of Systems Engineering and Electronics, vol. 21, no. 3, pp. 423-430, June 2010.
- Manel Mejri and Nadia Ben Azzouna. Scalable and Self-Adaptive Service Selection Method for the Internet of Things. International Journal of Computer Applications 167(10):43-49, June 2017.
- T. Miranda Lakshmi, V. Prasanna Venkatesan, A. Martin,"An Identification of Better Engineering College with Conflicting Criteria using Adaptive TOPSIS", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.5, pp.19-31, 2016.DOI: 10.5815/ijmecs.2016.05.03
- Wenyu Zhang, Shixiong Zhang, Shuai Zhang, Dejian Yu,” A novel method for MCDM and evaluation of manufacturing services using collaborative filtering and IVIF theory“,Journal of Algorithms & Computational TechnologyVol 10, Issue 1, pp. 40 – 51
- Hamdani Hamdani, Retantyo Wardoyo, Khabib Mustofa, "A Method of Weight Update in Group Decision-Making to Accommodate the Interests of All the Decision Makers", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.8, pp.1-10, 2017. DOI: 10.5815/ijisa.2017.08.01
- Topsis, sept, 2017. Retrived from, https://en.wikipedia.org/wiki/TOPSIS
- Dataset, Jan, 2017. Retrived from, https://www.yelp.com/dataset_challenge/dataset
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