Recommendation algorithm based on GMM clustering and FOA-GRNN model - Yipeng LI; Yeli RUAN; Jie ZHANG (网络与信息安全学报)
Aiming at the problem of low recommendation accuracy caused by sparse data in traditional item-based recommendation algorithm,a recommendation algorithm based on GMM clustering and FOA-GRNN model is proposed.The algorithm firstly uses Gaussian mixture model (GMM) to cluster the item features,then constructs the score matrix according to the clustering results,and fills the score matrix with slope one algorithm.Finally,the user's score based on similarity prediction is taken as input,and the final score is output through FOA-GRNN model.Experimental results based on movielens-2k dataset show that the proposed algorithm can deal with highly sparse data better and has better recommendation accuracy than the other three algorithms.