Abstract To solve the problem that the traditional collaborative filtering recommendation algorithm can not recommend with multiple criteria, a multicriteria recommendation algorithm based on Widrow-Hoff neural network is proposed by introducing the concept of multicriteria rating for extending the standard collaborative filtering algorithm. The Widrow-Hoff least mean square adaptive algorithm has the characteristics of hith 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. Measuring users' similarity by adopting the user preferences eigenvector and spatial distance matrix so as to locate a neighbor set for the best recommendations. Experimental results show that the proposed algorithm improves the accuracy and the quality of recommendation.
