User and Item based Collaborative Filtering Using Classification Property Naive Bayesian

분류 속성과 Naive Bayesian을 이용한 사용자와 아이템 기반의 협력적 필터링

  • Published : 2007.11.30


The collaborative filtering has used the nearest neighborhood method based on the preference and the similarity using the Pearson correlation coefficient. Therefore, it does not reflect content of the items and has the problems of the sparsity and scalability as well. the item-based collaborative filtering has been practically used to improve these defects, but it still does not reflect attributes of the item. In this paper, we propose the user and item based collaborative filtering using the classification property and Naive Bayesian to supplement the defects in the existing recommendation system. The proposed method complexity refers to the item similarity based on explicit data and the user similarity based on implicit data for handing the sparse problem. It applies to the Naive Bayesian to the result of reference. Also, it can enhance the accuracy as computation of the item similarity reflects on the correlative rank among the classification property to reflect attributes.


Collaborative Filtering;Naive Bayesian;Data Mining