Abstract: Due to the sparsity of user rating data and the lack of context information,the recommendation algorithm based on matrix factorization is often lacking in accuracy.To solve this problem,a recommendation algorithm based on convolutional neural network and constrained probability matrix factorization is proposed.Firstly,a convolutional neural network model is constructed to identify the contextual auxiliary information of users,obtain the text potential vector,superimpose gaussian noise,and initialize the proJect characteristic matrix.Then,according to the user rating information,the user characteristics are constrained by the constraint matrix,and the user characteristic matrix is initialized by superimposing the compensation matrix.Then,the initialized user characteristic matrix and proJect characteristic matrix are used to fit the rating matrix,the rating matrix is decomposed by matrix,and the coordinate descent algorithm is used to update the parameters.Finally,predict the user’s score on the proJect and implement the proJect recommendation.Experimental results on Movielens and Amazon data sets show that this recommendation algorithm is significantly superior to the traditional recommendation model and effectively improves the accuracy of recommendation results.

Key words: Contextual information, Convolutional neural networks, Matrix factorization, Recommendation algorithm