Recommendation based on object typicality

Yi Car; Ho Fung Leung; Qing Li; Jie Tang; Juanzi Li

doi:10.1145/1871437.1871664

Recommendation based on object typicality

Yi Car, Ho Fung Leung, Qing Li, Jie Tang, Juanzi Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

5 Citations (Scopus)

Abstract

Current recommendation methods are mainly classified into content-based, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users. In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicality-based recommendation approach. Experiments show that our method outperforms compared methods on recommendation quality.

Original language	English
Title of host publication	CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
Pages	1529-1532
Number of pages	4
DOIs	https://doi.org/10.1145/1871437.1871664
Publication status	Published - 1 Dec 2010
Externally published	Yes
Event	19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada Duration: 26 Oct 2010 → 30 Oct 2010

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
Country/Territory	Canada
City	Toronto, ON
Period	26/10/10 → 30/10/10

Keywords

Object typicality
Recommendation system

ASJC Scopus subject areas

General Decision Sciences
General Business,Management and Accounting

More information

10.1145/1871437.1871664

Cite this

@inproceedings{2facea80e43843c9ad65c34e37db6213,

title = "Recommendation based on object typicality",

abstract = "Current recommendation methods are mainly classified into content-based, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users. In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicality-based recommendation approach. Experiments show that our method outperforms compared methods on recommendation quality.",

keywords = "Object typicality, Recommendation system",

author = "Yi Car and Leung, {Ho Fung} and Qing Li and Jie Tang and Juanzi Li",

year = "2010",

month = dec,

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doi = "10.1145/1871437.1871664",

language = "English",

isbn = "9781450300995",

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pages = "1529--1532",

booktitle = "CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops",

note = "19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 ; Conference date: 26-10-2010 Through 30-10-2010",

}

Car, Y, Leung, HF, Li, Q, Tang, J & Li, J 2010, Recommendation based on object typicality. in CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops. International Conference on Information and Knowledge Management, Proceedings, pp. 1529-1532, 19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10, Toronto, ON, Canada, 26/10/10. https://doi.org/10.1145/1871437.1871664

Recommendation based on object typicality. / Car, Yi; Leung, Ho Fung; Li, Qing et al.
CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops. 2010. p. 1529-1532 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Li, Qing

AU - Tang, Jie

AU - Li, Juanzi

PY - 2010/12/1

Y1 - 2010/12/1

N2 - Current recommendation methods are mainly classified into content-based, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users. In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicality-based recommendation approach. Experiments show that our method outperforms compared methods on recommendation quality.

AB - Current recommendation methods are mainly classified into content-based, collaborative filtering and hybrid methods. These methods are based on similarity measurements among items or users. In this paper, we investigate recommendation systems from a new perspective based on object typicality and propose a novel typicality-based recommendation approach. Experiments show that our method outperforms compared methods on recommendation quality.

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KW - Recommendation system

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DO - 10.1145/1871437.1871664

M3 - Conference article published in proceeding or book

AN - SCOPUS:78651309001

SN - 9781450300995

T3 - International Conference on Information and Knowledge Management, Proceedings

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BT - CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops

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ER -

Recommendation based on object typicality

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

More information

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