Author(s)

Zijiang Zhu

South China Business College, Guangdong University of Foreign Studies, Guangzhou, China.

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.

Collaborative Filtering Algorithm, Genetic Algorithm, Parameter Combination Optimization

Zhu, Z. (2018) Research on Parameter Optimization in Collaborative Filtering Algorithm. Communications and Network, 10, 105-116. doi: 10.4236/cn.2018.103009.