Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions

Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions (2005)

by Gediminas Adomavicius , Alexander Tuzhilin

Venue:	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Citations:	381 - 2 self

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Datum	Value	Source
TITLE	Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions	INFERENCE
AUTHOR NAME	Gediminas Adomavicius	user correction
AUTHOR NAME	Alexander Tuzhilin	user correction
ABSTRACT	This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.	user correction
YEAR	2005	INFERENCE
VENUE	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	734--749	INFERENCE
VOLUME	17	INFERENCE
NUMBER	6	user correction
CITATIONS	112 found	ParsCit 1.0