为解决传统的协同过滤推荐算法不能综合运用多个指标进行推荐的问题,通过引人多指标评分的概念对标准的协同过滤推荐算法进行扩展,提出一种基于Widrow-Hoff神经网络的多指标推荐算法.利用Widrow-Hoff最小二乘法自适应算法在进行系统辨识时的高精度拟合特性,提出一种基于Widrow-Hoff最小二乘法算法的用户偏好特征向量计算方法.利用用户偏好特征向量和空间距离矩阵度量用户相似度,以定位邻居集并为用户推荐最优项目.实验结果表明,本文算法可提高推荐精度,改进推荐质量.%To solve the problem that the traditional collaborative filtering recommendation algorithm can not recommend with multiple criteria, a multi-criteria recommendation algorithm based on Widrow-Hoff neural network is proposed by introducing the concept of multi-criteria rating for extending the standard collaborative filtering algorithm. The Widrow-Hoff least mean square (LMS) adaptive algorithm has the characteristics of high accuracy fitting in the process of system identification. Based on that, an approach to compute user preferences eigenvector based on Widrow-Hoff LMS algorithm is proposed. The user preferences eigenvector and spatial distance are adopted to measure user similarity and then a neighbor set for the best recommendations is located. Experimental results show that the proposed algorithm improves the accuracy and the quality of recommendation.
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