A Cluster Based Collaborative Filtering Method for Improving the Performance of Recommender Systems in E-Commerce

被引：0

作者：

Sassani , Bahman ^{[1
]}

Alahmadi, Alaa ^{[1
]}

Sharifzadeh, Hamid ^{[1
]}

机构：

[1] Unitec Inst Technol, Dept Comp Sci, Auckland, New Zealand

来源：

PROCEEDINGS OF THE FUTURE TECHNOLOGIES CONFERENCE (FTC) 2018, VOL 2 | 2019年 / 881卷

关键词：

E-Commerce; Recommender systems; Collaborative filtering; Clustering; Genetic algorithm;

D O I：

10.1007/978-3-030-02683-7_73

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Rapid growth of E-commerce has made a huge number of products and services accessible to the users. The vast variety of options makes it difficult for the users to finalize their decisions. Recommender systems aim at offering the most suitable items to the users. In this paper, a collaborative filtering recommender system, called CFGA, is proposed which consists of two phases: offline and online. In the offline phase, users are clustered based on their similarities using genetic algorithm; and in the online phase, items which are interesting for a user's cluster members are recommended to that user. CFGA is evaluated with Movielens dataset and experimental results show that CFGA outperforms several existing recommendation methods in terms of accuracy.

引用

页码：990 / 1001

页数：12

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