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http://dx.doi.org/10.9708/jksci.2014.19.5.061

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

Lee, O-Joun (Dept. of Software science, Dankook University)
Baek, Yeong-Tae (Dept. of Multimedia, Kimpo University)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.5, 2014 , pp. 61-69 More about this Journal
Abstract
Collaborative filtering recommendation creates similar item subset or similar user subset based on 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 based Data Reliability. Preference prediction is carried out by creating similar item subset and similar user subset and predicting user preference by each subset and merging each predictive value by weighting point applying model condition. According to this technique, we can increase accuracy of user preference prediction and implement recommendation system which can provide highly reliable recommendation when density of preference matrix is low. Efficiency of this system is verified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than Hao Ji's technique when preference matrix sparsity is more than 84% through experiment.
Keywords
Recommendation; Collaborative Filtering; Preference Prediction;
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Times Cited By KSCI : 4  (Citation Analysis)
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