Integrating feature information for improving accuracy of collaborative filtering

Hyungil Kim; Juntae Kim; Jonathan L. Herlocker

doi:10.1007/978-3-540-28633-2_136

Integrating feature information for improving accuracy of collaborative filtering

Hyungil Kim
, Juntae Kim
, Jonathan L. Herlocker

Department of Computer Science and Artificial Intelligence

Research output: Contribution to journal › Conference article › peer-review

Abstract

Collaborative filtering (CF) has been widely used and successfully applied to recommend items in practical applications. However, the collaborative filtering has two inherent problems: data sparseness and the cold-start problems. In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome those difficulties and improve the accuracy of recommendation. We apply a two-pass method, first filling in unknown preference values, then generating the top-N recommendations.

Original language	English
Pages (from-to)	1005-1006
Number of pages	2
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3157
DOIs	https://doi.org/10.1007/978-3-540-28633-2_136
State	Published - 2004
Event	8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence - Auckland, New Zealand Duration: 9 Aug 2004 → 13 Aug 2004

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10.1007/978-3-540-28633-2_136

Cite this

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title = "Integrating feature information for improving accuracy of collaborative filtering",

abstract = "Collaborative filtering (CF) has been widely used and successfully applied to recommend items in practical applications. However, the collaborative filtering has two inherent problems: data sparseness and the cold-start problems. In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome those difficulties and improve the accuracy of recommendation. We apply a two-pass method, first filling in unknown preference values, then generating the top-N recommendations.",

author = "Hyungil Kim and Juntae Kim and Herlocker, \{Jonathan L.\}",

year = "2004",

doi = "10.1007/978-3-540-28633-2\_136",

language = "English",

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journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

note = "8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence ; Conference date: 09-08-2004 Through 13-08-2004",

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Integrating feature information for improving accuracy of collaborative filtering. / Kim, Hyungil; Kim, Juntae; Herlocker, Jonathan L.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3157, 2004, p. 1005-1006.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Integrating feature information for improving accuracy of collaborative filtering

AU - Kim, Hyungil

AU - Kim, Juntae

AU - Herlocker, Jonathan L.

PY - 2004

Y1 - 2004

N2 - Collaborative filtering (CF) has been widely used and successfully applied to recommend items in practical applications. However, the collaborative filtering has two inherent problems: data sparseness and the cold-start problems. In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome those difficulties and improve the accuracy of recommendation. We apply a two-pass method, first filling in unknown preference values, then generating the top-N recommendations.

AB - Collaborative filtering (CF) has been widely used and successfully applied to recommend items in practical applications. However, the collaborative filtering has two inherent problems: data sparseness and the cold-start problems. In this paper, we propose a method of integrating additional feature information of users and items into CF to overcome those difficulties and improve the accuracy of recommendation. We apply a two-pass method, first filling in unknown preference values, then generating the top-N recommendations.

UR - https://www.scopus.com/pages/publications/22944482590

U2 - 10.1007/978-3-540-28633-2_136

DO - 10.1007/978-3-540-28633-2_136

M3 - Conference article

AN - SCOPUS:22944482590

SN - 0302-9743

VL - 3157

SP - 1005

EP - 1006

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence

Y2 - 9 August 2004 through 13 August 2004

ER -

Integrating feature information for improving accuracy of collaborative filtering

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