Search

Recently, some attempts have been made in introducing deep neural networks (DNNs) to recommender systems for generating more accurate prediction due to the nonlinear representation learning capability of DNNs. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily suffer from the overfitting issue. To tackle these issues, we propose a novel recommendation framework based on Back Propagation (BP) neural network with attention mechanism, namely BPAM++. In particular, the BP neural network is utilized to learn the complex relationship between the target user and his/her neighbors and the complex relationship between the target item and its neighbors. Compared with DNNs, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also alleviate the overfitting issues in DNNs caused by a relatively small number of ratings. In addition, an attention mechanism is designed to capture the global impact of the nearest users of the target user on their nearest target user sets. Extensive experiments conducted on eight benchmark datasets confirm the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In recent years, many researchers take advantage of graph convolution network in multi-behavior recommendation to further alleviate the data sparsity problem. However, most of current works directly use graph convolution network, which makes time complexity of the model relatively high. These works also ignore the different weights of neighbors and the different contributions of each behavior to user' s preference . Therefore, this paper proposed a multi-behavior recommendation model based on light graph convolution and enhanced attention ( MB-LGCA) . Firstly, the model constructed a user-item bipartite graph according to the multi-behavior data, and used a light graph convolution network to aggregate the features of neighbors to obtain high-order collaborative information. At the same time, it used attention mechanism to integrate the neighbors' weights to enhance embedding representations of nodes . It used the k-order user 's embedding propagation to obtain the different importance of each behavior to user ' s preference, so that the model had better interpretability. Finally, it combined embedding representations of different layers for prediction. The experimental results on two real datasets show that the model has better performance. [ABSTRACT FROM AUTHOR]

For the high dimension and insufficient sample of side information and user comment input in the recommendation algorithm, this paper proposed an attentive collaborative side variational autoencoder recommender model (sVAE-a) for the nonlinear modeling ability of variational autoencoder and the related data enhancement ability of the attention mechanism. This model used collaborative side variational autoencoder to model the side information. At the same time, it used the attention mechanism to integrate the side information into the collaborative variational autoencoder architecture to strengthen the hidden variables and provide the decoder with cleaner features. At last, it used the variational inference to approximate the side information and the user's comments, and obtained the recommended model through the parameters training. The experimental results on the MovieLens-20M dataset show that the method can improve not only from the basic recall rate index, but also from the coverage rate and normalized damage cumulative gain (NOCG) index. The model is easy to implement. It can combine different types of input and side information, and improve recommendation performance. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs), with their promising potential to learn effective graph representation, have been widely used for recommender systems, in which the given graph data contains abundant users, items, and their historical interaction information. How to obtain preferable latent representations for both users and items is one of the key issues for GNN-based recommendation. This paper develops a novel deep GNN model with multi-graph attention fusion, MAF-GNN. This framework constructs two feature graph attention modules and a multi-scale latent features module, to generate better user and item latent features from input information. Specifically, the dual-branch residual graph attention (DBRGA) module is presented to extract neighbors' similar features from user and item graphs effectively and easily. Then multi-scale latent matrices are captured by applying non-linear transformations which are embedded to reduce the cost of dimension selection. Furthermore, a hybrid fusion graph attention (HFGA) module is designed to obtain valuable collaborative information from the user–item interaction graph, aiming to further refine the latent embedding of users and items. Finally, the whole MAF-GNN framework is optimized by a geometric factorized regularization loss. Extensive experiment results on both synthetic and real-world datasets illustrate that MAF-GNN can achieve better recommendation performance with a certain level of interpretability than some existing approaches. • A novel deep GNN model with multi-graph attention fusion mechanism is proposed. • A dual-branch residual graph attention module is developed. • A hybrid fusion graph attention module is designed to obtain valuable information. • Multi-scale latent matrices are designed to reduce significantly the time cost. [ABSTRACT FROM AUTHOR]