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Hybrid Preference Prediction Technique Using Weighting based Data Reliability for Collaborative Filtering Recommendation System

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2014, 19(5), pp.61-69
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

O-Joun Lee 1 BAEK, Yeong Tae 2

1단국대학교
2김포대학교

Accredited

ABSTRACT

Collaborative filtering recommendation creates similar item subset or similar user subset basedon user preference about items and predict user preference to particular item by using them. Thus,if preference matrix has low density, reliability of recommendation will be sharply decreased. To solve these problems we suggest Hybrid Preference Prediction Technique Using Weighting basedData Reliability. Preference prediction is carried out by creating similar item subset and similaruser subset and predicting user preference by each subset and merging each predictive value byweighting point applying model condition. According to this technique, we can increase accuracy ofuser preference prediction and implement recommendation system which can provide highlyreliable recommendation when density of preference matrix is low. Efficiency of this system isverified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than HaoJi's technique when preference matrix sparsity is more than 84% through experiment.

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