结合矩阵分解和延伸相似度的最近邻算法
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摘要
为有效解决目前绝大多数推荐系统存在数据稀疏、个性化低、计算负荷量大等特点,在最近邻(KNN)模型基础上提出一种结合降维的最近邻算法(KNN-DR),利用矩阵分解的方法降低矩阵的稀疏性,过程中融合更多隐式因子并加快运算速度;在皮尔逊相似度基础上添加延伸相似度,进一步克服数据稀疏性问题。该算法有效解决计算复杂度高和推荐效果大众化的缺点。实验结果表明,KNN-DR算法在推荐准确度上取得了更好的效果。
To solve the problems of sparse data,low personalization,large calculation and etc.in most of current recommended systems,based on the K nearest neighbor(KNN)model,a K nearest neighbor algorithm(KNN-DR)was proposed.Matrix decomposition was used to reduce the sparsity of the Matrix,more implicit factors were incorporated and the computation speed was accelerated.The extended similarity was added on the basis of Pearson similarity,which overcame the problem of sparse data.The proposed algorithm effectively solves the shortcomings of high computational complexity and popular recommendation.Experimental results show that the KNN-DR algorithm has better effects on the recommended accuracy.
To solve the problems of sparse data,low personalization,large calculation and etc.in most of current recommended systems,based on the K nearest neighbor(KNN)model,a K nearest neighbor algorithm(KNN-DR)was proposed.Matrix decomposition was used to reduce the sparsity of the Matrix,more implicit factors were incorporated and the computation speed was accelerated.The extended similarity was added on the basis of Pearson similarity,which overcame the problem of sparse data.The proposed algorithm effectively solves the shortcomings of high computational complexity and popular recommendation.Experimental results show that the KNN-DR algorithm has better effects on the recommended accuracy.
引文
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[14]Breese JS,Heckean an detal fanpirical analysis of predictive a1gorithms for collaborative F filtering[C]//Proc of the 14th Conference on Uncertainty in Artificial lntelligence,2008:43-52.
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