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.
Highlights
In recent years, with the rapid development of Internet technology, the ways for users to obtain data have become more and more abundant
When on ML-10M, the RMSE of AGAMF and AGAMF-N is increased by 1.46% and 0.8% compared with the GRU-Attention Matrix Factorization (GAMF) model, it shows that adversarial learning can effectively reduce the interference of model training by model parameters, thereby improving model performance
Due to the data sparsity problem in traditional recommendation systems, the recommendation model is extremely vulnerable to the interference of its parameters, and the additional stacked denoising autoencoder lacks the ability to extract deep features and key information of the context
Summary
With the rapid development of Internet technology, the ways for users to obtain data have become more and more abundant. XIA et al.: Collaborative Filtering Recommendation Algorithm based on Attention GRU and Adversarial Learning good at extracting effective latent features from auxiliary side information and obtaining the implicit relationship between users and items It extends the Stacked Denoising Autoencoder [11], takes additional auxiliary side information as input and integrates it closely with matrix factorization. Since the input document of the AutoEncoder contains many noise data without keywords, it is impossible to automatically distinguish keywords and capture sequence information, and it is extremely vulnerable to be interfered by model parameters during model training In response to these problems, based on Liu [17], a recommendation model (Adversarial GRUAttention Matrix Factorization, AGAMF) is proposed by combining adversarial learning and GRU-Attention mechanism. N R (u + Δu) (v + Δv), σ (1) In which N(x|μ, σ ) is the probability density function of the Gaussian normal distribution with mean μ and variance σ
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
