Improving the effectiveness of experiential decisions by recommendation systems

被引：5

作者：

Lin, Arthur J. ^{[1
]}

Hsu, Chien-Lung ^{[2
]}

Li, Eldon Y. ^{[3
]}

机构：

[1] Natl Taipei Univ, Taipei 23741, Taiwan

[2] Takming Univ Sci & Technol, Taipei 11451, Taiwan

[3] Natl Chengci Univ, Taipei 11605, Taiwan

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2014年 / 41卷 / 10期

关键词：

Recommendation system; Experiential decision; Multilayer perception model; Neural network system; Collaborative filtering system;

D O I：

10.1016/j.eswa.2014.01.035

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Providing experience-oriented offerings through e-commerce is an issue increasing critical in the growing commoditization of e-commercial services. The high accuracy of predictions rendered by Recommendation System (RS) technologies has strengthened the opportunities for experience-oriented offerings, making RS application an effective way of assisting consumers in online decision-making. This study proposes a RS for movie lovers using neural networks in collaborative filtering systems for consumers' experiential decisions. The experimental results reveal that it not only improves the accuracy of predicting movie ratings but also increases data transfer rates and provides richer user experiences. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：4904 / 4914

页数：11

共 35 条

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