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http://dx.doi.org/10.3745/KIPSTB.2009.16-B.1.55

A Combined Forecast Scheme of User-Based and Item-based Collaborative Filtering Using Neighborhood Size  

Choi, In-Bok (단국대학교 컴퓨터과학 및 통계학과)
Lee, Jae-Dong (단국대학교 컴퓨터학부)
Publication Information
The KIPS Transactions:PartB / v.16B, no.1, 2009 , pp. 55-62 More about this Journal
Abstract
Collaborative filtering is a popular technique that recommends items based on the opinions of other people in recommender systems. Memory-based collaborative filtering which uses user database can be divided in user-based approaches and item-based approaches. User-based collaborative filtering predicts a user's preference of an item using the preferences of similar neighborhood, while item-based collaborative filtering predicts the preference of an item based on the similarity of items. This paper proposes a combined forecast scheme that predicts the preference of a user to an item by combining user-based prediction and item-based prediction using the ratio of the number of similar users and the number of similar items. Experimental results using MovieLens data set and the BookCrossing data set show that the proposed scheme improves the accuracy of prediction for movies and books compared with the user-based scheme and item-based scheme.
Keywords
Recommender Systems; Memory-Based Collaborative Filtering; Combined Forecast;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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