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

Adomavicius, G., Tuzhilin, A.·2005·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/tkde.2005.99
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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 multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Metadata

Authors
Adomavicius, G., & Tuzhilin, A.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2005-06
Volume
17
Issue
6
Pages
734-749
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/tkde.2005.99
OpenAlex
W2171960770
Cited by
10,310
References
1
FWCI
489.36
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. https://app.cochat.ai/papers/10.1109/tkde.2005.99
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Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2005.99

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