关键词: 协同过滤, 欧氏距离, 归一化, 杰卡德相似系数

Abstract: User evaluation data of items often are of the biodiversity and sparse characteristic in collaborative filtering recommendation system, the traditional similarity measurement algorithm cannot effectively find similar neighbors, this paper proposed a neighbor similarity computing algorithm based on optimized Euclidean distance. The algorithm introduced normalization and Jaccard similarity coefficient based on Euclidean distance calculation, and finally made the evaluation prediction and recommendation. The experiments result on typical dataset show that the algorithm can effectively improve the performance of collaborative filtering recommendation system.
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Key words: collaborative filtering, Euclidean distance, normalized, Jaccard similarity coefficient