Content-based recommender system for online stores using expert system

被引：7
|
作者：
Walek, Bogdan [1 ]
Spackova, Petra [1 ]
机构：
[1] Univ Ostrava, Dept Informat & Comp, Ostrava, Czech Republic
来源：
2018 IEEE FIRST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND KNOWLEDGE ENGINEERING (AIKE) | 2018年
关键词：
content-based system; recommender system; online store; expert system; adaptivity;
D O I：
10.1109/AIKE.2018.00036
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper deals with a content-based recommender system for online stores using the expert system. We propose an algorithm which adapts the content based on user preferences and the content viewed by the user. The main goal of the recommender system is to propose and deliver suitable content to the user. One of the goals of the proposed recommender system is to decrease the cold start effect. At the end of the paper, the proposed system is experimentally verified.
引用
收藏
页码：164 / 165
页数：2
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