Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms

Deepshikha Sethi, Abhishek Singhal
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引用次数: 5

Abstract

Recommender systems are the backbone of electronic commerce sites like amazon.in, netflix and flipkart.com which not only helps in achieving better customer satisfaction but also helps in bringing those products into the notice of the customer which are not easily seen by the customer but it helps in increasing the business of such e-commerce sites. This paper present a movie recommender system that uses collaborative filtering technique of recommender system and apply Ant Colony Optimization and Artificial Bee Colony Optimization and also compare the two algorithms on the basis of CPU Time and two standard functions.
基于蚁群优化算法和人工蜂群优化算法的推荐系统比较分析
推荐系统是亚马逊等电子商务网站的支柱。在Netflix和flipkart.com中,这不仅有助于实现更好的客户满意度,而且有助于将这些产品带入客户的注意,这些产品不容易被客户看到,但它有助于增加此类电子商务网站的业务。本文利用推荐系统的协同过滤技术,提出了一种电影推荐系统,应用蚁群算法和人工蜂群算法,并在CPU时间和两个标准函数的基础上对这两种算法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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