Exploiting Group Information for Personalized Recommendation with Graph Neural Networks

2022 ◽  
Vol 40 (2) ◽  
pp. 1-23
Author(s):  
Zhiqiang Tian ◽  
Yezheng Liu ◽  
Jianshan Sun ◽  
Yuanchun Jiang ◽  
Mingyue Zhu
Keyword(s):  
Neural Networks ◽  
Online Social Networks ◽  
Historical Data ◽  
User Preferences ◽  
Discussion Groups ◽  
Personalized Recommendation ◽  
Target User ◽  
Group Information ◽  
Graph Neural Networks ◽  
The Relationship

Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared

scholarly journals Top-N personalized recommendation with graph neural networks in MOOCs

2021 ◽  
Vol 2 ◽  
pp. 100010
Author(s):  
Jingjing Wang ◽  
Haoran Xie ◽  
Fu Lee Wang ◽  
Lap-Kei Lee ◽  
Oliver Tat Sheung Au
Keyword(s):  
Neural Networks ◽  
Personalized Recommendation ◽  
Graph Neural Networks

scholarly journals CFGNN: Cross Flow Graph Neural Networks for Question Answering on Complex Tables

2020 ◽  
Vol 34 (05) ◽  
pp. 9596-9603
Author(s):  
Xuanyu Zhang
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Question Answering ◽  
Cross Flow ◽  
Context Information ◽  
Flow Graph ◽  
Graph Neural Networks ◽  
Relationship Of ◽  
The Relationship ◽  
Parent Child

Question answering on complex tables is a challenging task for machines. In the Spider, a large-scale complex table dataset, relationships between tables and columns can be easily modeled as graph. But most of graph neural networks (GNNs) ignore the relationship of sibling nodes and use summation as aggregation function to model the relationship of parent-child nodes. It may cause nodes with less degrees, like column nodes in schema graph, to obtain little information. And the context information is important for natural language. To leverage more context information flow comprehensively, we propose novel cross flow graph neural networks in this paper. The information flows of parent-child and sibling nodes cross with history states between different layers. Besides, we use hierarchical encoding layer to obtain contextualized representation in tables. Experiments on the Spider show that our approach achieves substantial performance improvement comparing with previous GNN models and their variants.

Combining user-end and item-end knowledge graph learning for personalized recommendation

2021 ◽  
pp. 1-13
Author(s):  
Tianlong Gu ◽  
Haohong Liang ◽  
Chenzhong Bin ◽  
Liang Chang
Keyword(s):  
Semantic Information ◽  
Structural Information ◽  
Auxiliary Information ◽  
Mutual Effect ◽  
User Preferences ◽  
User Preference ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Graph Learning ◽  
Target User

How to accurately model user preferences based on historical user behaviour and auxiliary information is of great importance in personalized recommendation tasks. Among all types of auxiliary information, knowledge graphs (KGs) are an emerging type of auxiliary information with nodes and edges that contain rich structural information and semantic information. Many studies prove that incorporating KG into personalized recommendation tasks can effectively improve the performance, rationality and interpretability of recommendations. However, existing methods either explore the independent meta-paths for user-item pairs in KGs or use a graph convolution network on all KGs to obtain embeddings for users and items separately. Although both types of methods have respective effects, the former cannot fully capture the structural information of user-item pairs in KGs, while the latter ignores the mutual effect between the target user and item during the embedding learning process. To alleviate the shortcomings of these methods, we design a graph convolution-based recommendation model called Combining User-end and Item-end Knowledge Graph Learning (CUIKG), which aims to capture the relevance between users’ personalized preferences and items by jointly mining the associated attribute information in their respective KG. Specifically, we describe user embedding from a user KG and then introduce user embedding, which contains the user profile into the item KG, to describe item embedding with the method of Graph Convolution Network. Finally, we predict user preference probability for a given item via multilayer perception. CUIKG describes the connection between user-end KG and item-end KG, and mines the structural and semantic information present in KG. Experimental results with two real-world datasets demonstrate the superiority of the proposed method over existing methods.

scholarly journals Data Capitalism: Efficiency as a sociability degree function

2020 ◽  
Vol 11 (2) ◽  
pp. 82
Author(s):  
Dora Kaufman
Keyword(s):  
Online Social Networks ◽  
Business Environment ◽  
User Preferences ◽  
Digital Data ◽  
Digital Platforms ◽  
Personal Lives ◽  
Technology Platforms ◽  
Degree Function ◽  
Operational Processes ◽  
The Relationship

The purpose of this paper is to address the relationship between sociability and efficiency in AI-drive models, in how contemporary economics has brought the notion of efficiency into our personal lives. Initially we introduced the basics of three key concepts of the article: data capitalism, data and deep learning. Next, we describe the exponential evolution of storage, processing and transmission technology showing that over the years, the ability to transform analog data into digital data has expanded exponentially. This capacity increased the efficiency of the operational processes with the measure of efficiency calculated and controlled against the maximum potential of the digital data produced in these interactions. For traditional firms, competing with digital rivals involves rearchitecting the firm’s organization and operating model. The compartmentalisation in silos compromises the efficiency of AI-drive models which demand integrated data base. The digital transformation requires huge investment in management, time and financial resources. However, it is the only way to remain competitive and survive in the 21st century market. The commitment to identify and measure user preferences and habits, and then to predict behaviour, is the logic behind technology platforms and applications, online social networks, e-commerce and search engines. Digital platforms are designed to extend the lifespan of their users, thereby generating greater engagement and more data. The originality of this paper is to correlate sociability and economic efficiency in the present business environment with a technological and social approach.

Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation

2022 ◽  
Vol 40 (2) ◽  
pp. 1-28
Author(s):  
Wei Zhang ◽  
Zeyuan Chen ◽  
Hongyuan Zha ◽  
Jianyong Wang
Keyword(s):  
Neural Networks ◽  
Data Driven ◽  
Seamless Integration ◽  
Specific Product ◽  
Product Recommendation ◽  
Target User ◽  
Complementary Graph ◽  
Sequential Product ◽  
Graph Neural Networks ◽  
Public Datasets

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

NEURAL NETWORK METHOD OF ADAPTING THE PARAMETERS OF THE INFORMATION SYSTEMS INTERFACE

2020 ◽  
pp. 26-31
Author(s):  
А.Д. Обухов ◽  
М.Н. Краснянский ◽  
М.С. Николюкин
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Statistical Processing ◽  
Low Complexity ◽  
User Preferences ◽  
Expert Assessment ◽  
Practical Implementation ◽  
Algorithmic Support ◽  
Interface Parameters

Рассматривается проблема выбора оптимальных параметров интерфейса в информационных системах с целью его персонализации под предпочтения пользователя и возможности его оборудования. В настоящее время для ее решения используется алгоритмическое обеспечение и статистическая обработка предпочтений пользователей, что не обеспечивает достаточной гибкости и точности. Поэтому в данной работе предлагается применение разработанного метода адаптации параметров интерфейса, основанного на анализе и обработке пользовательской информации с помощью нейронных сетей. Научная новизна метода заключается в автоматизации сбора, анализа данных и настройки интерфейса за счет использования и интеграции нейронных сетей в информационную систему. Рассмотрена практическая реализация предлагаемого метода на Python. Экспертная оценка адаптивности интерфейса тестовой информационной системы после внедрения разработанного метода показала его перспективность и эффективность. Разработанный метод показывает лучшую точность и низкую сложность программной реализации относительно классического алгоритмического подхода. Полученные результаты могут использоваться для автоматизации процесса выбора компонентов интерфейса различных информационных систем. Дальнейшие исследования заключаются в развитии и интеграции разработанного метода в рамках фреймворка адаптации информационных систем Here we consider the problem of choosing the optimal parameters of the interface in information systems with the aim of personalizing it for the preferences of the user and the capabilities of his equipment. Currently, algorithmic support and statistical processing of user preferences are used to solve it, which does not provide sufficient flexibility and accuracy. Therefore, in this work, we propose the application of the developed method for adapting interface parameters based on the analysis and processing of user information using neural networks. The scientific novelty of the method is to automate the collection, analysis of data and interface settings through the use and integration of neural networks in the information system. We consider the practical implementation of the proposed method in Python. An expert assessment of the adaptability of the interface of the test information system after the implementation of the developed method showed its availability and efficiency. The developed method shows the best accuracy and low complexity of software implementation relative to the classical algorithmic approach. The results obtained can be used to automate the selection of interface components for various information systems. Further research consists in the development and integration of the developed method within the framework of the information systems adaptation framework

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks
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