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

has been cited by the following article:

• TITLE: Research on Parameter Optimization in Collaborative Filtering Algorithm

  AUTHORS: Zijiang Zhu

  KEYWORDS: Collaborative Filtering Algorithm, Genetic Algorithm, Parameter Combination Optimization

  JOURNAL NAME: Communications and Network, Vol.10 No.3, August 27, 2018

  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.