Abstract 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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2011, Vol. 24 
