Improving prediction quality in collaborative filtering based on clustering

Taek Hun Kim, Seok In Park, Sung Bong Yang

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Pages704-710
Number of pages7
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia
Duration: 2008 Dec 92008 Dec 12

Publication series

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

Other

Other2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
CountryAustralia
CitySydney, NSW
Period08/12/908/12/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Improving prediction quality in collaborative filtering based on clustering'. Together they form a unique fingerprint.

  • Cite this

    Kim, T. H., Park, S. I., & Yang, S. B. (2008). Improving prediction quality in collaborative filtering based on clustering. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 (pp. 704-710). [4740533] (Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008). https://doi.org/10.1109/WIIAT.2008.319