Abstract
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.
Highlights
With the development of society and science, people gradually enter the era of information overloaded from the era of lack of 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
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
Summary
With the development of society and science, people gradually enter the era of information overloaded from the era of lack of information. The model uses a convolutional neural networks (CNN) with self-attention mechanism to process auxiliary information It can capture both the context information and the long-distance dependence between different components of auxiliary information. Mao et al [19] used a CNN that considered time information to extract information from auxiliary data on users and items separately, and used the fully connected network to predict the score of the two This model alleviates the data sparsity and can learn the trends in user preferences and item attributes that change over time, to better capture the interaction between users and items. The ConvMF model uses a CNN to process documents’ auxiliary information It can capture the context information in the auxiliary data, it cannot capture the long-distance dependence between different components of auxiliary data. This paper uses this model to process auxiliary data and combines the model with PMF
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