Improving prediction quality in collaborative filtering based on clustering

Taek Hun Kim; Seok In Park; Sung Bong Yang

doi:10.1109/WIIAT.2008.319

Improving prediction quality in collaborative filtering based on clustering

Taek Hun Kim, Seok In Park, Sung Bong Yang

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite the fact that they are similar customers, by properly converting data before performing clustering. The second method explains the k-prototype algorithm performing clustering by expanding not only the numeric data but also the categorical data. The experimental results show that better prediction quality can be obtained when both methods are used together.

Original language	English
Title of host publication	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Pages	704-710
Number of pages	7
DOIs	https://doi.org/10.1109/WIIAT.2008.319
Publication status	Published - 2008
Event	2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia Duration: 2008 Dec 9 → 2008 Dec 12

Publication series

Name	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

Other

Other	2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Country	Australia
City	Sydney, NSW
Period	08/12/9 → 08/12/12

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/WIIAT.2008.319

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Cite this

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title = "Improving prediction quality in collaborative filtering based on clustering",

abstract = "In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite the fact that they are similar customers, by properly converting data before performing clustering. The second method explains the k-prototype algorithm performing clustering by expanding not only the numeric data but also the categorical data. The experimental results show that better prediction quality can be obtained when both methods are used together.",

author = "Kim, {Taek Hun} and Park, {Seok In} and Yang, {Sung Bong}",

year = "2008",

doi = "10.1109/WIIAT.2008.319",

language = "English",

isbn = "9780769534961",

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Kim, TH, Park, SI & Yang, SB 2008, Improving prediction quality in collaborative filtering based on clustering. in Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008., 4740533, Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, pp. 704-710, 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008, Sydney, NSW, Australia, 08/12/9. https://doi.org/10.1109/WIIAT.2008.319

Improving prediction quality in collaborative filtering based on clustering. / Kim, Taek Hun; Park, Seok In; Yang, Sung Bong.

Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008. 2008. p. 704-710 4740533 (Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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