scholarly journals Knowledge-Enhanced Graph Neural Networks for Sequential Recommendation

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 388
Author(s):  
Baocheng Wang ◽  
Wentao Cai
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Internet Technology ◽  
Sequential Data ◽  
Current Interest ◽  
Important Method ◽  
Real World Datasets ◽  
Set Up ◽  
Graph Neural Networks ◽  
Better Than

With the rapid increase in the popularity of big data and internet technology, sequential recommendation has become an important method to help people find items they are potentially interested in. Traditional recommendation methods use only recurrent neural networks (RNNs) to process sequential data. Although effective, the results may be unable to capture both the semantic-based preference and the complex transitions between items adequately. In this paper, we model separated session sequences into session graphs and capture complex transitions using graph neural networks (GNNs). We further link items in interaction sequences with existing external knowledge base (KB) entities and integrate the GNN-based recommender with key-value memory networks (KV-MNs) to incorporate KB knowledge. Specifically, we set a key matrix to many relation embeddings that learned from KB, corresponding to many entity attributes, and set up a set of value matrices storing the semantic-based preferences of different users for the corresponding attribute. By using a hybrid of a GNN and KV-MN, each session is represented as the combination of the current interest (i.e., sequential preference) and the global preference (i.e., semantic-based preference) of that session. Extensive experiments on three public real-world datasets show that our method performs better than baseline algorithms consistently.

Current Interest Enhanced Graph Neural Networks for Session-based Recommendation

2021 ◽  
Author(s):  
Yujie Lu ◽  
Yayu Kong ◽  
Zhongtian Sun ◽  
Ze Zhang ◽  
Tingwei Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Current Interest ◽  
Graph Neural Networks

scholarly journals Fast and Deep Graph Neural Networks

2020 ◽  
Vol 34 (04) ◽  
pp. 3898-3905 ◽  
Author(s):  
Claudio Gallicchio ◽  
Alessio Micheli
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Neural Networks ◽  
State Of The Art ◽  
Input Graph ◽  
Sparse Networks ◽  
Set Up ◽  
The Stability ◽  
Graph Neural Networks ◽  
Architectural Organization

We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.

scholarly journals Session centered Recommendation Utilizing Future Contexts in Social Media

2021 ◽  
Vol 29 (3) ◽  
pp. 91-104
Author(s):  
Sanjeev Dhawan ◽  
Kulvinder Singh ◽  
Adrian Rabaea ◽  
Amit Batra
Keyword(s):  
Data Augmentation ◽  
Empirical Studies ◽  
Sequential Data ◽  
Space Filling ◽  
The Future ◽  
Future Data ◽  
Real World Datasets ◽  
Sequential Information ◽  
Better Than

Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender (SRec), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network (CNN) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.

scholarly journals UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

2021 ◽  
Author(s):  
Jing Huang ◽  
Jie Yang
Keyword(s):  
Neural Networks ◽  
Message Passing ◽  
State Of The Art ◽  
Representation Learning ◽  
Graph Representation ◽  
Challenging Problem ◽  
Unified Framework ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Research Domains

Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4% to 88.8% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at https://github.com/OneForward/UniGNN.

scholarly journals Learning Graph Neural Networks with Positive and Unlabeled Nodes

2021 ◽  
Vol 15 (6) ◽  
pp. 1-25
Author(s):  
Man Wu ◽  
Shirui Pan ◽  
Lan Du ◽  
Xingquan Zhu
Keyword(s):  
Neural Networks ◽  
Short Distance ◽  
Attention Mechanism ◽  
Long Distance ◽  
Model Learning ◽  
Transductive Learning ◽  
Single Class ◽  
Aggregate Information ◽  
Real World Datasets ◽  
Graph Neural Networks

Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdependency between nodes. To enable GNN learning, existing works typically assume that labeled nodes, from two or multiple classes, are provided, so that a discriminative classifier can be learned from the labeled data. In reality, this assumption might be too restrictive for applications, as users may only provide labels of interest in a single class for a small number of nodes. In addition, most GNN models only aggregate information from short distances ( e.g. , 1-hop neighbors) in each round, and fail to capture long-distance relationship in graphs. In this article, we propose a novel GNN framework, long-short distance aggregation networks, to overcome these limitations. By generating multiple graphs at different distance levels, based on the adjacency matrix, we develop a long-short distance attention model to model these graphs. The direct neighbors are captured via a short-distance attention mechanism, and neighbors with long distance are captured by a long-distance attention mechanism. Two novel risk estimators are further employed to aggregate long-short-distance networks, for PU learning and the loss is back-propagated for model learning. Experimental results on real-world datasets demonstrate the effectiveness of our algorithm.

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

2019 ◽  
Vol 31 (7) ◽  
pp. 1235-1270 ◽  
Author(s):  
Yong Yu ◽  
Xiaosheng Si ◽  
Changhua Hu ◽  
Jianxun Zhang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Cell Structure ◽  
Relevant Information ◽  
Future Research ◽  
Sequential Data ◽  
Learning Capacity ◽  
Research Areas ◽  
Almost All

Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the relevant information of input data when the input gap is large. By introducing gate functions into the cell structure, the long short-term memory (LSTM) could handle the problem of long-term dependencies well. Since its introduction, almost all the exciting results based on RNNs have been achieved by the LSTM. The LSTM has become the focus of deep learning. We review the LSTM cell and its variants to explore the learning capacity of the LSTM cell. Furthermore, the LSTM networks are divided into two broad categories: LSTM-dominated networks and integrated LSTM networks. In addition, their various applications are discussed. Finally, future research directions are presented for LSTM networks.

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 Fast and effective protein model refinement by deep graph neural networks

2020 ◽  
Author(s):  
Xiaoyang Jing ◽  
Jinbo Xu
Keyword(s):  
Neural Networks ◽  
Structure Prediction ◽  
Initial Model ◽  
Model Quality ◽  
Model Refinement ◽  
Protein Model ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Improved Model ◽  
Better Than

AbstractProtein structure prediction has been greatly improved, but there are still a good portion of predicted models that do not have very high quality. Protein model refinement is one of the methods that may further improve model quality. Nevertheless, it is very challenging to refine a protein model towards better quality. Currently the most successful refinement methods rely on extensive conformation sampling and thus, takes hours or days to refine even a single protein model. Here we propose a fast and effective method for protein model refinement with very limited conformation sampling. Our method applies GNN (graph neural networks) to predict refined inter-atom distance probability distribution from an initial model and then rebuilds the model using the predicted distance as restraints. On the CASP13 refinement targets our method may refine models with comparable quality as the two leading human groups (Feig and Baker) and greatly outperforms the others. On the CASP14 refinement targets our method is only second to Feig’s method, comparable to Baker’s method and much better than the others (who worsened instead of improved model quality). Our method achieves this result by generating only 5 refined models for an initial model, which can be done in ~15 minutes. Our study also shows that GNN performs much better than convolutional residual neural networks for protein model refinement when conformation sampling is limited.AvailabilityThe code will be released once the manuscript is published and available at http://[email protected]

Sign in / Sign up

Export Citation Format

Share Document