Link Prediction based on bipartite graph for recommendation system using optimized SVD++

Gupta, Anshul; Shrinath, Pravin

doi:10.1016/j.procs.2023.01.114

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Study on personalized recommendation algorithm based on knowledge association Explore methods to improve interpretability and explain ability of the recommendation process to enhance user trust, [68] Link prediction based on bipartite graph and optimized SVD++ Users (Users No. are not mentioned)…”

Section: Methodologies/ Techniquesmentioning

confidence: 99%

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M.,

Goudar,

Kulkarni

et al. 2024

IEEE Access

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This review delves into using e-learning technology and personalized recommendation systems in education. It examines 60 articles from prominent databases and identifies the different methods used in recommendation systems, such as collaborative and content-based approaches with a recent shift towards machine learning. However, the current personalized recommendation system faces challenges such as a lack of understanding of the content, student discontinuity, language barriers, confusion in selecting study materials, inadequate infrastructure and funding. The review proposes using new digital technologies to address these issues, including Fluxy AI, Twin technology, AI-powered virtual proctoring, and Alter Ego. These technologies can create a dynamic and interactive learning environment, providing tailored learning experiences for students and insights for educators to provide targeted support and guidance. The integration of these technologies can improve individualized learning, increase understanding capacity and enhance the learning experience for students with speech disorders.

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Section: Methodologies/ Techniquesmentioning

confidence: 99%

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

G. M.,

Goudar,

Kulkarni

et al. 2024

IEEE Access

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show abstract

“…For instance, they facilitate the discovery of previously unknown associations between genes and diseases [29,33,35], as well as the relationships between drugs and disease treatment [27,31]. Additionally, link prediction is valuable in recommender systems [15,20,36], aiding in suggesting individuals for communication purposes in social networks or in the establishment of communication infrastructure such as data centers, roads, railways, and airline flights [26].…”

Section: Introductionmentioning

confidence: 99%

Introducing new Link Prediction Measures and an Evaluation Metric

Javan,

Moeini,

Ghodousian

2024

Preprint

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Link prediction plays a critical role in network analysis as it tackles the task of predicting missing or future connections within a given network. A wide array of link prediction measures has been proposed to estimate the likelihood of link existence between nodes, aiming to uncover hidden relationships and anticipate the formation of new connections. This task holds particular significance in various domains, including social networks, biological networks, transportation networks, and recommender systems. Nonetheless, evaluating the effectiveness of these measures poses challenges, encompassing both the measures themselves and the employed evaluation metrics. This article offers a comprehensive overview of the existing link prediction measures, shedding light on their limitations and addressing the associated challenges in evaluating their efficacy. Moreover, we propose enhancements to overcome these challenges, entailing modifications to existing measures and the introduction of novel evaluation metrics. By tackling these issues head-on, our objective is to enhance the accuracy and reliability of link prediction in network analysis.

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Beyond classical approaches: redefining the landscape of high-accurate movie recommendation using QNN

Sinha,

Priye

2024

J Supercomput

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Link Prediction based on bipartite graph for recommendation system using optimized SVD++

Cited by 5 publications

References 35 publications

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

A Digital Recommendation System for Personalized Learning to Enhance Online Education: A Review

Introducing new Link Prediction Measures and an Evaluation Metric

Beyond classical approaches: redefining the landscape of high-accurate movie recommendation using QNN

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