Collaborative Filtering Recommendation Algorithm Based on Attention GRU and Adversarial Learning

Abstract

Aiming at the problem that the traditional collaborative filtering algorithm using shallow models cannot learn the deep features of users and items, and the recommendation model is very susceptible to the counter-interference of its parameters; this paper proposes a matrix-factorization recommendation model that combines adversarial learning and attention-gated recurrent units (AGAMF). Firstly, the gated recurrent unit based on the attention mechanism is used to extract the user's latent vector from the user's auxiliary side information. Secondly, the convolutional neural network is used to extract the item's latent vector from the item's auxiliary side information. Finally, adversarial disturbances are introduced on the latent factors of users and items to quantify the loss of the model under parameter disturbances, and the latent vectors of users and items are integrated into the probability matrix factorization to predict the user's rating of the item. Experiments were performed on two real data sets MovieLens-1M and MovieLens-10M, and the RMSE, MAE and Recall indicators were used for evaluation. Experiments prove that the model proposed in this paper is robust and can effectively alleviate the problem of data sparsity. Compared with other related recommendation algorithms, our model has a significant improvement in recommendation performance.

Keywords

• Adversarial learning
• attention mechanism
• gated recurrent unit
• convolutional neural network
• probabilistic matrix factorization
• collaborative filtering