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A Strategy for Neighborhood Selection in Collaborative Filtering-based Recommender Systems
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  • Journal title : Journal of KIISE
  • Volume 42, Issue 11,  2015, pp.1380-1385
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2015.42.11.1380
 Title & Authors
A Strategy for Neighborhood Selection in Collaborative Filtering-based Recommender Systems
Lee, Soojung;
 
 Abstract
Collaborative filtering is one of the most successfully used methods for recommender systems and has been utilized in various areas such as books and music. The key point of this method is selecting the most proper recommenders, for which various similarity measures have been studied. To improve recommendation performance, this study analyzes problems of existing recommender selection methods based on similarity and presents a method of dynamically determining recommenders based on the rate of co-rated items as well as similarity. Examination of performance with varying thresholds through experiments revealed that the proposed method yielded greatly improved results in both prediction and recommendation qualities, and that in particular, this method showed performance improvements with only a few recommenders satisfying the given thresholds.
 Keywords
collaborative filtering;recommender system;similarity measure;nearest neighbor;
 Language
Korean
 Cited by
1.
협력필터링의 데이터 희소성 해결을 위한 자카드 지수 반영의 유사도 성능 분석,이수정;

컴퓨터교육학회논문지, 2016. vol.19. 4, pp.59-66
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