The collaborative filtering recommendation based on SOM cluster-indexing CBR

Tae Hyup Roh, Kyong Joo Oh, Ingoo Han

Research output: Contribution to journalArticle

125 Citations (Scopus)

Abstract

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model, which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, Self-Organizing Map (SOM) and Case Based Reasoning (CBR) by changing an unsupervized clustering problem into a supervized user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

Original languageEnglish
Pages (from-to)413-423
Number of pages11
JournalExpert Systems with Applications
Volume25
Issue number3
DOIs
Publication statusPublished - 2003 Oct 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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