scholarly journals A Rating Prediction Recommendation Model Combined with the Optimizing Allocation for Information Granularity of Attributes

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 21
Author(s):  
Jianfei Li ◽  
Yongbin Wang ◽  
Zhulin Tao
Keyword(s):  
Neural Network ◽  
Latent Factors ◽  
Latent Factor ◽  
Information Granularity ◽  
Node Attribute ◽  
Item Attribute ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Granularity Distribution

In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for training and learning. A user–item graph, a user–attribute graph, and an item–attribute graph are constructed according to the interactions between users and items. The latent factors of users and items can be learned in these graph structure data. There are many methods for learning the latent factors of users and items. Still, they do not fully consider the influence of node attribute information on the representation of the latent factors of users and items. We propose a rating prediction recommendation model, short for LNNSR, utilizing the level of information granularity allocated on each attribute by developing a granular neural network. The different granularity distribution proportion weights of each attribute can be learned in the granular neural network. The learned granularity allocation proportion weights are integrated into the latent factor representation of users and items. Thus, we can capture user-embedding representations and item-embedding representations more accurately, and it can also provide a reasonable explanation for the recommendation results. Finally, we concatenate the user latent factor-embedding and the item latent factor-embedding and then feed it into a multi-layer perceptron for rating prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework.

scholarly journals Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks

2018 ◽  
Author(s):  
Xiaotian Han ◽  
Chuan Shi ◽  
Senzhang Wang ◽  
Philip S. Yu ◽  
Li Song
Keyword(s):  
Neural Network ◽  
Collaborative Filtering ◽  
Factor Models ◽  
Neural Network Models ◽  
Latent Factors ◽  
Latent Factor ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Latent Features ◽  
Latent Factor Models

Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the rating information between users and items, although some recently extended models add some auxiliary information to learn a unified latent factor between users and items.  The unified latent factor only represents the latent features of users and items from the aspect of purchase history. However, the latent features of users and items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling rich objects and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items through different meta-paths and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are effectively fused with an attention mechanism for the top-N recommendation. Extensive experiments on three real datasets show that NeuACF significantly outperforms both existing latent factor models and recent neural network models.

scholarly journals Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing

2022 ◽  
Vol 16 (2) ◽  
pp. 1-18
Author(s):  
Hanlu Wu ◽  
Tengfei Ma ◽  
Lingfei Wu ◽  
Fangli Xu ◽  
Shouling Ji
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
State Of The Art ◽  
Superior Performance ◽  
True Label ◽  
Label Aggregation ◽  
Correlation Information ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
High Level

Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, a label aggregation model that infers the true label from noisy crowdsourced labels is required. In this article, we propose a novel framework based on graph neural networks for aggregating crowd labels. We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels. Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks. Experimental results on 13 real-world datasets show superior performance over state-of-the-art models.

scholarly journals MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

2019 ◽  
Author(s):  
Nuo Xu ◽  
Pinghui Wang ◽  
Long Chen ◽  
Jing Tao ◽  
Junzhou Zhao
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Dual Graph ◽  
Material Design ◽  
Drug Regimen ◽  
Short Term ◽  
New Material ◽  
Long Short Term Memory ◽  
Real World Datasets ◽  
Graph Neural Networks

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs, and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present {\em MR-GNN}, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.

scholarly journals NeuRec: On Nonlinear Transformation for Personalized Ranking

2018 ◽  
Author(s):  
Shuai Zhang ◽  
Lina Yao ◽  
Aixin Sun ◽  
Sen Wang ◽  
Guodong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Real World ◽  
Real Life ◽  
Interaction Matrix ◽  
Interaction Patterns ◽  
Latent Factors ◽  
Integrated Network ◽  
Ranking Task ◽  
Real World Datasets ◽  
Personalized Ranking

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: user-based NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.

scholarly journals Memory Augmented Graph Neural Networks for Sequential Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 5045-5052 ◽  
Author(s):  
Chen Ma ◽  
Liheng Ma ◽  
Yingxue Zhang ◽  
Jianing Sun ◽  
Xue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Performance Metrics ◽  
Contextual Information ◽  
Short Term ◽  
User Interests ◽  
User Behaviors ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Occurrence Patterns

The chronological order of user-item interactions can reveal time-evolving and sequential user behaviors in many recommender systems. The items that users will interact with may depend on the items accessed in the past. However, the substantial increase of users and items makes sequential recommender systems still face non-trivial challenges: (1) the hardness of modeling the short-term user interests; (2) the difficulty of capturing the long-term user interests; (3) the effective modeling of item co-occurrence patterns. To tackle these challenges, we propose a memory augmented graph neural network (MA-GNN) to capture both the long- and short-term user interests. Specifically, we apply a graph neural network to model the item contextual information within a short-term period and utilize a shared memory network to capture the long-range dependencies between items. In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items. We extensively evaluate our model on five real-world datasets, comparing with several state-of-the-art methods and using a variety of performance metrics. The experimental results demonstrate the effectiveness of our model for the task of Top-K sequential recommendation.

scholarly journals Metric Factorization with Item Cooccurrence for Recommendation

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 512 ◽  
Author(s):  
Honglin Dai ◽  
Liejun Wang ◽  
Jiwei Qin
Keyword(s):  
Matrix Factorization ◽  
Euclidean Distance ◽  
Metric Learning ◽  
Sparse Data ◽  
Inner Product ◽  
Interaction Matrix ◽  
Latent Factors ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Item Item

In modern recommender systems, matrix factorization has been widely used to decompose the user–item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user–item interaction matrix and the item–item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.

Collaboration Matrix Factorization on Rate and Review for Recommendation

2019 ◽  
Vol 30 (2) ◽  
pp. 27-43
Author(s):  
Zhicheng Wu ◽  
Huafeng Liu ◽  
Yanyan Xu ◽  
Liping Jing
Keyword(s):  
Neural Networks ◽  
Recommender Systems ◽  
Projection Method ◽  
Matrix Factorization ◽  
Auxiliary Information ◽  
Latent Factors ◽  
Latent Factor ◽  
Real World Datasets ◽  
Rating Information

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.

Mobile App Cross-Domain Recommendation with Multi-Graph Neural Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-21
Author(s):  
Yi Ouyang ◽  
Bin Guo ◽  
Xing Tang ◽  
Xiuqiang He ◽  
Jian Xiong ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Apps ◽  
Rapid Development ◽  
Representation Learning ◽  
Adaptive Method ◽  
Mobile App ◽  
Cross Domain ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Novel Model

With the rapid development of mobile app ecosystem, mobile apps have grown greatly popular. The explosive growth of apps makes it difficult for users to find apps that meet their interests. Therefore, it is necessary to recommend user with a personalized set of apps. However, one of the challenges is data sparsity, as users’ historical behavior data are usually insufficient. In fact, user’s behaviors from different domains in app store regarding the same apps are usually relevant. Therefore, we can alleviate the sparsity using complementary information from correlated domains. It is intuitive to model users’ behaviors using graph, and graph neural networks have shown the great power for representation learning. In this article, we propose a novel model, Deep Multi-Graph Embedding (DMGE), to learn cross-domain app embedding. Specifically, we first construct a multi-graph based on users’ behaviors from different domains, and then propose a multi-graph neural network to learn cross-domain app embedding. Particularly, we present an adaptive method to balance the weight of each domain and efficiently train the model. Finally, we achieve cross-domain app recommendation based on the learned app embedding. Extensive experiments on real-world datasets show that DMGE outperforms other state-of-art embedding methods.

scholarly journals Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 403
Author(s):  
Xun Zhang ◽  
Lanyan Yang ◽  
Bin Zhang ◽  
Ying Liu ◽  
Dong Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Supervised Learning ◽  
Structured Data ◽  
Similarity Matrix ◽  
Scale Feature ◽  
Multi Scale ◽  
Adjacent Node ◽  
Feature Similarity ◽  
The Mean ◽  
Graph Neural Networks

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.

Sign in / Sign up

Export Citation Format

Share Document