Bilateral personalized attention recommendation algorithm for user data based on neural network

Abstract

There have been two major obstacles in the process of recommender systems. One is the cold start problem, and the other is the data sparse problem. How to solve these two obstacles is the main direction that needs to be improved and resolved in the field of recommendation systems. The paper designs a network model based on UIEncoder-DAMLPARL and proposes a deep learning recommendation method that combines user reviews and ratings with bilateral personalized attention mechanism. It is proposed that learn the characteristics of user reviews on the basis of convolutional neural networks, so as to enhance the interpretability in recommendations. Then the scoring features and comment features are integrated into a unified neural network model through the DAML model, and learn the representation of the user or item from the comments through the user/item encoder, so as to enhance the personalized recommendation. Finally, the high-order nonlinear interaction between the features is realized through the neural factorization machine, so as to achieve the final score prediction. In addition, to solve the problem of data sparseness, the PARL model, combined with our model, assists in modeling when there are few user reviews and product reviews. It is verified in the experiments that our method can effectively improve the performance of the recommender system on Amazon data sets.