A collaborative filtering algorithm embedded BP network to ameliorate sparsity issue

Collaborative filtering technologies are facing two major challenges: scalability and recommendation quality. Sparsity of source data sets is one major reason causing the poor recommendation quality. To reduce sparsity, we design a collaborative filtering algorithm who firstly selects users whose non-null ratings intersect the most as candidates of nearest neighbors, and then builds up backpropagation neural networks to predict values of the null ratings in the candidates. Experimental results show that this algorithm can increase the accuracy of nearest neighbors, resulting in improving recommendation quality of the recommendation system.