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Research on Parameter Optimization in Collaborative Filtering Algorithm
South China Business College, Guangdong University of Foreign Studies, Guangzhou, China
- 1 South China Business College, Guangdong University of Foreign Studies, Guangzhou, China
Communications and Network·Volume 10 (2018)·Pages 105–116·Published 13 July 2018·DOI10.4236/cn.2018.103009
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Abstract
Collaborative filtering algorithm is the most widely used and recommended algorithm in major e-commerce recommendation systems nowadays. Concerning the problems such as poor adaptability and cold start of traditional collaborative filtering algorithms, this paper is going to come up with improvements and construct a hybrid collaborative filtering algorithm model which will possess excellent scalability. Meanwhile, this paper will also optimize the process based on the parameter selection of genetic algorithm and demonstrate its pseudocode reference so as to provide new ideas and methods for the study of parameter combination optimization in hybrid collaborative filtering algorithm.
KeywordsCollaborative Filtering AlgorithmGenetic AlgorithmParameter Combination Optimization
- Zhang, J.W. and Yang, Z. (2014) Collaborative Filtering Recommendation Algorithm Based on Improved User Clustering. Computer Science, 41, 176-178. https://doi.org/10.11896/j.issn.1002-137X.2014.12.038
- Xiao, Q., Zhu, Q.H., Zheng, H. and Wu, K.W. (2013) Design and Implementation of Distributed Collaborative Filtering Algorithm on Hadoop. Data Analysis and Knowledge Discovery, No. 1, 83-89.
- Zhang, L., Teng, P.Q. and Qin, T. (2014) Using Key Users of Social Network to Enhance Collaborative Filtering Performance. Journal of Intelligence, 33, 196-200.
- Wen, S.Q., Wang, C., Su, F.F., Liu, J.F., Chen, Y.W. and Zheng, G.Q. (2017) Using Users’ Unfavorable Item Attributes to Improve the Efficiency and Accuracy of Item-Based Collaborative Filtering Algorithm. Journal of Chinese Computer Systems, 38, 1735-1740.
- Wang, J.H. and Han, J.T. (2017) Collaborative Filtering Algorithm Based on Item Attribute Preference. Computer Engineering and Applications, 53, 106-110.
- Zhu, B. (2017) The Improvement and Empirical Analysis of the Collaborative Filtering Algorithm about the Recommendation System of Digital Library. Library and Information Service, 61, 130-134. https://doi.org/10.13266/j.issn.0252-3116.2017.09.017
- Zhong, D.H., Du, R.X., Cui, B., Wu, B.P. and Guan, T. (2018) Real-Time Spreading Thickness Monitoring of High-Core Rockfill Dam Based on K-Nearest Neighbor Algorithm. Transactions of Tianjin University, 24, 282-289. https://doi.org/10.1007/s12209-017-0115-5
- Chen, A.P. and Wang, S. (2014) A Hybrid Collaborative Filtering Algorithm Based on User-Item. Computer Technology and Development, 24, 88-91.
- Stacewicz, P. (2015) Evolutionary Schema of Modeling Based on Genetic Algorithms. Studies in Logic. Grammar and Rhetoric, 40, 219-239. https://content.sciendo.com/view/journals/slgr/40/1/article-p219.xml https://doi.org/10.1515/slgr-2015-0011
- Kuzelewska, U. (2014) Clustering Algorithms in Hybrid Recommender System on Movie Lens Data. Studies in Logic, Grammar and Rhetoric, 37, 125-139. https://doi.org/10.2478/slgr-2014-0021
- Wang, W.J. and Lu, Y.M. (2018) Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, No. 1, 1-10. http://iopscience.iop.org/article/10.1088/1757-899X/324/1/012049/meta https://doi.org/10.1088/1757-899X/324/1/012049
- Jafari, M., Ghavami, B. and Sattari, V. (2017) A Hybrid Framework for Reverse Engineering of Robust Gene Regulatory Networks. Artificial Intelligence in Medicine, No. 6, 15-27. https://linkinghub.elsevier.com/retrieve/pii/S0933365716304882 https://doi.org/10.1016/j.artmed.2017.05.004