Hybrid recommendation algorithm based on deep neural network and probabilistic matrix factorization

Abstract:

Aiming at the facts that user and project description information is not fully utilized in personalized recommendation and user score matrix data set is extremely sparse, a hybrid recommendation algorithm based on deep neural network and probabilistic matrix factorization (PMF) is proposed. Firstly, user and item description information is preprocessed to form user and item feature sets containing user preference, and then each feature is fed into the deep neural network model for training. At the same time, the probabilistic matrix decomposition model is used to optimize the potential eigenvectors based on the maximum posterior estimation of the user score matrix. Then the potential feature vectors of the probabilistic matrix model and the real feature vectors of the deep neural network model are iteratively updated to converge to the potential feature vectors that fuse the real information of the user and the item. Finally, this feature vector is used to make personalized recommendation to users. Experiments show that the proposed algorithm is better than the classical recommendation algorithm and previous algorithms in term of the mean square error and mean absolute error index, which shows the effectiveness of the proposed algorithm.