A weighted sifting method to improve the effectiveness of collaborative filtering

Tuguldur Sumiya; Heungsun Park; Jonghoon Chun; Zoonky Lee; Sang Goo Lee; Eugene Kim; Donghoon Shin; Won Gyu Lee; Jinwook Choi; Juno Chang

A weighted sifting method to improve the effectiveness of collaborative filtering

Tuguldur Sumiya, Heungsun Park, Jonghoon Chun, Zoonky Lee, Sang Goo Lee, Eugene Kim, Donghoon Shin, Won Gyu Lee, Jinwook Choi, Juno Chang

Department of Computer Science and Engineering

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

In this paper, we improve the accuracy of the conventional collaborative filtering algorithm by proposing a weighted sifting method. The weighted sifting method preprocesses the given customer data to generate an adjusted customer data which we believe contains less noise than the original one, and thus effectively discriminates the preference weights of items for each customer. We present two alternative calculation methods for weight adjustment, and our experimental evaluation shows that both calculation methods result in better accuracy than traditional collaborative filtering.

Original language	English
Pages	B266-B269
Publication status	Published - 2004
Event	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering - Chiang Mai, Thailand Duration: 2004 Nov 21 → 2004 Nov 24

Other

Other	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering
Country/Territory	Thailand
City	Chiang Mai
Period	04/11/21 → 04/11/24

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Cite this

A weighted sifting method to improve the effectiveness of collaborative filtering. / Sumiya, Tuguldur; Park, Heungsun; Chun, Jonghoon et al.
2004. B266-B269 Paper presented at IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering, Chiang Mai, Thailand.

Research output: Contribution to conference › Paper › peer-review

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abstract = "In this paper, we improve the accuracy of the conventional collaborative filtering algorithm by proposing a weighted sifting method. The weighted sifting method preprocesses the given customer data to generate an adjusted customer data which we believe contains less noise than the original one, and thus effectively discriminates the preference weights of items for each customer. We present two alternative calculation methods for weight adjustment, and our experimental evaluation shows that both calculation methods result in better accuracy than traditional collaborative filtering.",

author = "Tuguldur Sumiya and Heungsun Park and Jonghoon Chun and Zoonky Lee and Lee, {Sang Goo} and Eugene Kim and Donghoon Shin and Lee, {Won Gyu} and Jinwook Choi and Juno Chang",

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AU - Sumiya, Tuguldur

AU - Park, Heungsun

AU - Chun, Jonghoon

AU - Lee, Zoonky

AU - Lee, Sang Goo

AU - Kim, Eugene

AU - Shin, Donghoon

AU - Lee, Won Gyu

AU - Choi, Jinwook

AU - Chang, Juno

PY - 2004

Y1 - 2004

N2 - In this paper, we improve the accuracy of the conventional collaborative filtering algorithm by proposing a weighted sifting method. The weighted sifting method preprocesses the given customer data to generate an adjusted customer data which we believe contains less noise than the original one, and thus effectively discriminates the preference weights of items for each customer. We present two alternative calculation methods for weight adjustment, and our experimental evaluation shows that both calculation methods result in better accuracy than traditional collaborative filtering.

AB - In this paper, we improve the accuracy of the conventional collaborative filtering algorithm by proposing a weighted sifting method. The weighted sifting method preprocesses the given customer data to generate an adjusted customer data which we believe contains less noise than the original one, and thus effectively discriminates the preference weights of items for each customer. We present two alternative calculation methods for weight adjustment, and our experimental evaluation shows that both calculation methods result in better accuracy than traditional collaborative filtering.

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T2 - IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering

Y2 - 21 November 2004 through 24 November 2004

ER -

A weighted sifting method to improve the effectiveness of collaborative filtering

Abstract

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ASJC Scopus subject areas

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