Attention neural collaboration filtering based on GRU for recommender systems

Abstract

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.

Interrogative Sentences Recognition Based on the GRU Multiattentive Layer Model

“…Early approaches to recognize statements included rule-based and machine-learning approaches. Reference [12] chose multi-layer perceptron neural networks and LSTM models to extract utterances in medical records; [13] used LSTM models to recognize utterances in the medical field; [14] based on CNN was used to identify utterances in combat documents. Reference [15] constructed a CRFs-based product alias recognition model for manually annotated ancient materials of Fangzhi; [16] recognized place names in Zuo Shi Zhuan of the Spring and Autumn Period based on CRFs and MEMM, and used e State Language [17]as a validation corpus, and found that CRFs outperformed MEMM [18] used a Bi LSTM-CNN-CRF model for entity extraction as the basis for constructing a Chinese historical knowledge graph.…”

Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

“…Time aware recommender systems are a subset of Context-aware recommender systems that focus on using time as contextual information to identify overlapping networks among users and aid in reducing the effect of sparsity [37]. Xia et al [38] proposes an attention based neural collaboration filtering for recommender systems, where long-distance dependent information and key information are provided as attention to the neural network to handle the cold start and data sparsity issues. Personalised recommendations is obtained using a autoencoder with attention mechanism, proposed in [9] where it gathers context information into the madel but lacks its accuracy in sparsity and cold start problem.…”

Deep metric attention learning for skin lesion classification in dermoscopy images

“…The attention mechanism in deep learning is similar to the human visual attention mechanism, whose goal is to select information that is more critical to the current task from a lot of information. It has brought many breakthroughs to the fields of natural language processing [28][29][30] and computer vision [31,32]. Recently, many attentionbased methods have been designed to strengthen the feature representation capabilities of CNNs in image classification tasks [33][34][35].…”