Collaborative Filtering Hybrid Recommendation Based on CNN and Association Rules
Aiming at the problems of traditional collaborative filtering algorithm on user evaluation and serious data sparseness in the field of drug recommendation,a collaborative filtering hybrid recommendation algorithm based on convolutional neural net-work(CNN)and association rules are proposed.First,CNN is used to obtain the deep efficacy feature vector from the drug text da-ta,and then the Apriori algorithm is used to explore the association rules between drugs.And on this basis,this paper combines the corresponding similarity calculation formula from the two perspectives of drug efficacy and relevance to calculate the drug similarity,and then predicts the missing value of the score,and finally superimposes and fills the sparse matrix to realize the optimization of drug recommendation.Compared with the experiment,the algorithm in this paper reduces the MAE and RMSE indicators by 3%to 4%compared with the traditional collaborative filtering algorithm,and it has a good recommendation effect when the data is sparse.
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