DESIGN PERSONALIZATION CLASSIFICATION MODEL USING SEMI-SUPERVISED SUPPORT VECTOR MACHINES
Abstract Internet user is growing everyday as an outcome of this huge volume of data is being produced constantly. Thus Mining and evaluating such data can support an organization in services extending from website personalization and usage classification. The pre-existing machine learning algorithms are unable to solve this in a better way. The current application for data classification is really expensive in nature. Improve the recommendation technique using map reduce model based on the machine learning. In this research work, we have proposed technique for Big Web Data Classification for User Behaviour Predicting using Fusion Data level S3VM. The experimental results show that Fusion Data level S3VM is appropriate for modelling a classification model among high accuracy and that its performance is better to that of traditional machine learning classification methods. Our proposed framework is based on data mining, machine learning with neural network algorithm which used for the user personalization classification and improving recommendation.
Rajani S. Sadasivam, Gayathri Sundar ,Murat M. Tanik, and Murat N. Tanju(2006) Process Personalization Framework for Service-driven Enterprises Southeast Con, Proceedings of the IEEE , doi: 10.1109/second.2006.1629342. pp.159-164.
Shuqing Li and Enlai Ma(2015) The Design of Web Portable Personalization Frameworl based on Iterative Profiling Algorithm with Time Unit of Weighted Keywords” International Conference on Behavioral, Economic, and Socio-Cultural Computing , Nanjing, China.
Xiaojian Ding, Yuancheng Li，Yinliang Zhao (2008),” A framework of user model based on Semi-supervised techniques” IEEE International Conference on e-Business Engineering- DOI 10.1109/ICEBE.2008.75.
urat Ali Bayir , Ismail Hakki Toroslu , Ahmet Cosar , Guven Fidan (2009),” Smart Miner: A New Framework for Mining Large Scale Web Usage Data” WWW 2009, April 20–24, 2009, Madrid, Spain. ACM 978-1-60558-487-4/09/04
Dimitrios Pierrakos,and Georgios Paliouras(2010)” Personalizing Web Directories with the Aid of Web Usage Data” ieee transactions on knowledge and data engineering, vol. 22, no. 9, 10.1109/TKDE.2009.173.
Magdalini Eirinaki, Joannis Vlachakis, and Sarabjot Singh Anand (2005) IKUM: An Integrated Web Personalization Platform Based on Content Structures and User Behavior ITWP 2003, LNAI 3169, Springer-Verlag Berlin Heidelberg pp. 272 – 288.
Wen, H., Xie, W., & Pei, J. (2014). A pre-Radical Basis Function with deep Back Propagation Neural Network research, 1489–1494.
Werwath, M. (2017). Implications of big data for data scientists and engineers. IEEE Engineering Management Review, 45(3), 82–83. https://doi.org/10.1109/EMR.2017.2734323
Wu, C.-Y., Alvino, C. V., Smola, A. J., & Basilico, J. (2016). Using Navigation to Improve Recommendations in Real-Time. Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, 341–348. https://doi.org/10.1145/2959100.2959174
Wu, C., Wu, J., Chen, Y., Xie, M., Guo, X., & Xiong, W. (2011). Personalized Learning Service Framwork Based on Semantic Web Technologies, (60672051), 2396–2399.
Wu, W., & Peng, M. (2017). A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network for Short-Term Wind Power Forecasting. IEEE Internet of Things Journal, 4(4), 979–986. https://doi.org/10.1109/JIOT.2017.2677578
Xia, D., Li, H., Wang, B., Li, Y., & Zhang, Z. (2016). A Map Reduce-Based Nearest Neighbor Approach for Big-Data-Driven Traffic Flow Prediction. IEEE Access, 4, 2920–2934. https://doi.org/10.1109/ACCESS.2016.2570021
Xia, D., Rong, Z., Zhou, Y., Wang, B., Li, Y., & Zhang, Z. (2013). Discovery and analysis of usage data based on hadoop for personalized information access. Proceedings - 16th IEEE International Conference on Computational Science and Engineering, CSE 2013, 917–924. https://doi.org/10.1109/CSE.2013.137
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