Recommendation Algorithm Based on Improved Convolutional Neural Network and Matrix Factorization

摘要

The traditional collaborative filtering algorithm uses the user rating information as a recommendation basis,but the ratings matrices are usually sparse and cannot reflect users’preference exactly,so the recommendation results are not very accurate.Therefore,this paper proposes an improved convolutional neural network for collaborative filtering(CNNCF),using the deep learning model to deeply mine the hidden feature information.then implicit the semantic model,Then the extracted explicit feature information was replaced by the implicit feature information in the LFM to further improve the prediction accuracy,and finally personalized recommendation through the user-item preference matrix.Experimental results on the MovieLens dataset show that the model can overcome data sparse,and recommendation accuracy is better than the traditional collaborative filtering model.