Addressing the Cold-Start Problem Using Data Mining Techniques and Improving Recommender Systems by Cuckoo Algorithm: A Case Study of Facebook

被引：24

作者：

Forouzandeh, Saman ^{[1
,2
]}

Aghdam, Atae Rezaei ^{[2
,3
]}

Forouzandeh, Soran ^{[2
,4
]}

Xu, Shuxiang ^{[5
]}

机构：

[1] Univ Appl Sci & Technol, Dept Comp Engn, Tehran, Iran

[2] Ctr Tehran Municipal ICT Org, Tehran, Iran

[3] Univ Appl Sci & Technol, Informat Technol Dept, Tehran, Iran

[4] Univ Appl Sci & Technol, Tehran, Iran

[5] Univ Tasmania, Sch Technol Environm & Design, Hobart, Tas, Australia

来源：

COMPUTING IN SCIENCE & ENGINEERING | 2020年 / 22卷 / 04期

关键词：

Facebook; Recommender systems; Data mining; Clustering algorithms; Optimization; Software; Recommender Systems; Data Mining; Social Networks; Cold-start; Cuckoo Algorithm;

D O I：

10.1109/MCSE.2018.2875321

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

One of the most common problems in recommender systems is a "cold-start" problem, which is related to users who do not indicate any behavior on social media. This paper proposes a solution for tackling this problem by using data mining techniques and improving the recommender systems by using the Cuckoo algorithm.

引用

页码：62 / 73

页数：12

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