Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors

George Lekakos, George M. Giaglis

Research output: Contribution to journalArticle

92 Citations (Scopus)

Abstract

Recommender systems are a special class of personalized systems that aim at predicting a user's interest on available products and services by relying on previously rated items or item features. Human factors associated with a user's personality or lifestyle, although potential determinants of user behavior are rarely considered in the personalization process. In this paper, we demonstrate how the concept of lifestyle can be incorporated in the recommendation process to improve the prediction accuracy by efficiently managing the problem of limited data availability. We propose two approaches: one relying on lifestyle alone and another integrating lifestyle within the nearest neighbor approach. Both approaches are empirically tested in the domain of recommendations for personalized television advertisements and are shown to outperform existing nearest neighborhood approaches in most cases.

Original languageEnglish
Pages (from-to)410-431
Number of pages22
JournalInteracting with Computers
Volume18
Issue number3
DOIs
Publication statusPublished - May 2006

Keywords

  • Advertisements
  • Collaborative filtering
  • Content-based filtering
  • Digital television
  • Lifestyle
  • Personalization
  • Recommenders systems

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