ImprovedGCN: An efficient and accurate recommendation system employing lightweight graph convolutional networks in social media

Dhawan, Sanjeev; Singh, Kulvinder; Rabaea, Adrian; Batra, Amit

doi:10.1016/j.elerap.2022.101191

Cited by 12 publications

(15 citation statements)

References 35 publications

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“…Recommender systems have become an inseparable component of online web applications by helping users find desired products and services (Sha et al ., 2021; Wu et al ., 2022a, b, c, d). Research confirms that soft computing methods greatly enhance the performance and efficiency of recommender systems (Dhawan et al ., 2022; Wang et al ., 2022a, b, c, d).…”

Section: Resultsmentioning

confidence: 66%

“…GCNs offer a comprehensive learning experience and, in turn, are adaptable enough to be implemented into various designs for deep learning; however, they have a key weakness in information dilution (Dhawan et al ., 2022). MAGDM aids in selecting the best option considering various factors, though it can suffer from information loss or misrepresentation when making complicated decisions (Liang et al ., 2019).…”

Section: Discussionmentioning

confidence: 99%

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Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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Section: Resultsmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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“…Graph Convolutional Neural Networks (GCNs) encompass two distinct approaches: spectral and spatial methods. Spectral methods delineate convolutions by leveraging the graph convolution theorem in the spectral domain [10] . In contrast, spatial methods perform convolutions directly in the node domain, employing aggregation functions.…”

Section: Graph Convolutional Neuralmentioning

confidence: 99%

Collaborative filtering recommendation method based on graph convolutional neural networks

Yuan,

Zhan,

Zhou

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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In the rapidly advancing information technology era, information overload poses a significant challenge. Recommender systems offer a partial solution, yet traditional methods grapple with issues like sparse data and accuracy. For this reason, this paper introduces a novel approach-a high-order graph convolutional collaborative filtering model. This model employs a subgraph generation module to enhance the importance of neighbor nodes during high-order graph convolutions. Our approach yields enhanced embeddings by embedding user-item interaction information using graph techniques, stacking multi-layer graph convolutional networks to capture complex interactions, and leveraging both initial and convoluted embeddings. This paper introduces a constraint loss function to address over-smoothing in graph-based recommendations. Our method's effectiveness is confirmed through extensive experiments on three real-world datasets.

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“…The GCN is the most frequently used GNN architecture in the literature due to its simplicity and effectiveness in various application domains and tasks [52]. In each layer, the node representation is updated according to the following propagation rules…”

Section: Graph Convolution Networkmentioning

confidence: 99%

GNN-LS: A Learning Style Prediction in Online Environments using Graph Neural Networks

Muhammad¹,

Liu²,

Ahmad³

et al. 2022

J-NaNA

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Prediction of learners’ learning styles in online environments has several advantages, including steering learners on the proper path, motivating and engaging them while learning, and improving their learning results. It also helps instructors in the formation of personalized resource recommendations. As a result, predicting learners learning styles is necessary to aid in the personalization process. Existing approaches use either conventional or automatic approaches for learning style identifications. However, the large volume of data stored in online platforms has become a challenge in analyzing the behavior of learners and predicting their learning styles in the real world. Also, most of the existing approaches rely on a particular learning platform and can not be used in other platforms without technical assistance. In this paper, we propose GNN-LS, a new approach to identify and predict learners learning styles using a graph neural network. First, the graph embedding technique is used to capture the representation of learners and resources as a bipartite graph and encode them into low-dimensional representation. The encoded L-R sequences were given as input to the K-means clustering algorithm to identify and obtain labels as per FSLSM dimensions. Then, Graph neural network is trained to predict the learner’s learning style in the real world. The GNN-LS technique can be applied in a variety of educational systems and adapted to fit a variety of learning style models. Extensive experiments are run using the 2015 KDD Cup public available dataset to demonstrate the capabilities of GNN-LS. 5.31-15.68% improvements are achieved across all four FSLSM dimensions in accuracy.

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ImprovedGCN: An efficient and accurate recommendation system employing lightweight graph convolutional networks in social media

Cited by 12 publications

References 35 publications

Soft computing in business: exploring current research and outlining future research directions

Soft computing in business: exploring current research and outlining future research directions

Collaborative filtering recommendation method based on graph convolutional neural networks

GNN-LS: A Learning Style Prediction in Online Environments using Graph Neural Networks

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