Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information. First, the self-attention mechanism is added to convolutional matrix factorization and a probabilistic matrix factorization model, based on a convolutional neural networks with self-attention mechanism, is proposed. Second, after integrating auxiliary information, such as item comment, item name, and item category, probabilistic matrix factorization, based on a self-attention mechanism convolutional neural networks, is used for recommendation. Adding the self-attention mechanism allows convolutional matrix factorization to capture the long-distance dependence between different components of the auxiliary information. Integrating the item comment, name, and category information alleviates the data sparsity of recommendation, and improves the accuracy of rating prediction. Experimental results on the MovieLens-1M and MovieLens-10M datasets show that the probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item comment, name, and category information is superior to existing popular methods, in respect of root mean square error.