Context-Aware Recommender Systems

被引：173
|
作者：
Adomavicius, Gediminas [1 ]
Mobasher, Bamshad [2 ]
Ricci, Francesco [3 ]
Tuzhilin, Alex [4 ]
机构：
[1] Univ Minnesota, Minneapolis, MN 55455 USA
[2] Depaul Univ, Ctr Web Intelligence, Sch Comp, Chicago, IL 60604 USA
[3] Free Univ Bozen Bolzano, Bolzano, Italy
[4] NYU, Stern Sch Business, New York, NY 10003 USA
来源：
AI MAGAZINE | 2011年 / 32卷 / 03期
基金：
美国国家科学基金会;
关键词：
INFORMATION;
D O I：
10.1609/aimag.v32i3.2364
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Context-aware recommender systems (CARS) generate more.relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches riff incorporating contextual information in the recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and figure research directions for context-aware recommender systems.
引用
收藏
页码：67 / 80
页数：14
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