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. INDEX TERMS adversarial learning, attention mechanism, gated recurrent unit, convolutional neural network, probabilistic matrix factorization, collaborative filtering.

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The collaborative filtering method is widely used in the traditional recommendation system. The collaborative filtering method based on matrix factorization treats the user’s preference for the item as a linear combination of the user and the item latent vectors, and cannot learn a deeper feature representation. In addition, the cold start and data sparsity remain major problems for collaborative filtering. To tackle these problems, some scholars have proposed to use deep neural network to extract text information, but did not consider the impact of long-distance dependent information and key information on their models. In this paper, we propose a neural collaborative filtering recommender method that integrates user and item auxiliary information. This method fully integrates user-item rating information, user assistance information and item text assistance information for feature extraction. First, Stacked Denoising Auto Encoder is used to extract user features, and Gated Recurrent Unit with auxiliary information is used to extract items’ latent vectors, respectively. The attention mechanism is used to learn key information when extracting text features. Second, the latent vectors learned by deep learning techniques are used in multi-layer nonlinear networks to learn more abstract and deeper feature representations to predict user preferences. According to the verification results on the MovieLens data set, the proposed model outperforms other traditional approaches and deep learning models making it state of the art.

As an important artificial implant material, the corrosion resistance of NiTi shape memory alloy is closely related to the machined surface quality. In this paper, the multiple analysis methods concerning potentiodynamic polarization, impedance spectrum and corrosion morphology are used to analyze the corrosion resistance of the alloy. The potentiodynamic polarization and impedance spectrum test results show that the conductivity and corrosion current density of electrochemical polishing surface decrease, and the polarization resistance and corrosion potential increase compared with milling. After electrochemical polishing, the surface roughness of the milling sample is decreased, and the NiTi alloy of austenite phase is transformed into TiO2, which improves the corrosion resistance of the alloy. In addition, there are pitting corrosion, hole corrosion and crevice corrosion morphology on the milling surface, while the pitting corrosion and hole corrosion exist on the electrochemical polishing surface. The corrosion morphology verified the analysis of potentiodynamic polarization and impedance spectrum. The multiple analysis method proposed in this paper can be used as a more accurate evaluation method for the corrosion resistance of alloy surface, avoiding the error of analysis results caused by the impedance spectrum equivalent circuit and potentiodynamic polarization following Tafel relationship.

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