基于Widrow-Hoff神经网络的多指标推荐算法  

Multi-Criteria Recommendation Algorithm Based on Widrow-Hoff Neural Network

作　　者：张付志[1] 常俊风[1] 王栋[1] 

机构地区：[1]燕山大学信息科学与工程学院,秦皇岛066004

出　　处：《模式识别与人工智能》2011年第2期233-242,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家重点基础研究发展计划(973计划)项目(No.2005CB321902);河北省自然科学基金项目(No.F2008000877;F2011203219);教育部科技发展中心网络时代的科技论文快速共享专项研究项目(No.20091333110011;20101333110013)资助

摘　　要：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,通过引入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.

关 键 词：Widrow—Hoff神经网络 推荐算法 多指标评分 相似度 用户偏好特征向量 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]