An Improved Restaurant Recommendation Algorithm Based on User's Multiple Features

被引：0

作者：

Shi, Yancui ^{[1
]}

Zhao, Qing ^{[1
]}

Wang, Yuan ^{[1
]}

Cao, Jianhua ^{[1
]}

机构：

[1] Tianjin Univ Sci & Technol, Coll Comp Sci & Informat Engn, Tianjin 300457, Peoples R China

来源：

PROCEEDINGS OF 2018 IEEE 9TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS) | 2018年

基金：

中国国家自然科学基金;

关键词：

recommender system; collaborative filtering; user preference; influence; userfeature;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

It is challenging to find the appropriate restaurant using diangping.com. In this paper, regarding the characteristics of restaurant recommender systems, the restaurant reconunendation based on the improved collaborative filtering method (lCFM) is proposed by analyzing the users features. The ICFM considers the influence of the user him-or herself, the similarity of user preferences and the follow relationship. Finally, an experhnent is executed on the data set crawled from dianping.com: dianP. The experlmental results show that the proposed method obtains better performance than the existing methods.

引用

页码：191 / 194

页数：4

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