A QoS prediction approach based on fusion of network representation learning and dynamic collaborative filtering for cloud service

Chen, Liqiong; Fan, Guoqing; Guo, Kun

doi:10.1504/ijiitc.2021.115708

Cited by 2 publications

(1 citation statement)

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“…Early research on network representation learning was mainly based on factorization methods. This type of method mainly adopts the idea of graph embedding to explore the manifold structure of the data (Chen et al, 2021). On the premise that the input data are independent and identically distributed, the graph embedding method believes that the data has a manifold structure, so the similarity between the data is calculated first.…”

Section: Literature Reviewmentioning

confidence: 99%

Learning Behavior Evaluation Model and Teaching Strategy Innovation by Social Media Network Following Learning Psychology

Yuan

et al. 2022

Front. Psychol.

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With the development of various network technologies and the spread of coronavirus disease 2019, many online learning platforms have been built. However, some of them may negatively impact student learning outcomes. Therefore, this study aims to improve the online learning effect of students by comprehensively evaluating their learning behavior by using deep learning algorithms. On this basis, new teaching strategies are proposed. According to the structured deep network embedding model, a network representation learning algorithm is proposed with the help of auto-encoders under deep learning. This study elaborates the concept and structure of the encoder model and tests its performance. After the node labels and dataset are trained, the applicable parameter λ2 of the model is 0.3. During the teaching process, the model’s reliability in distinguishing users is examined. Therefore, this model can be applied to network teaching, is an innovative teaching strategy, and provides a theoretical basis for improving teaching methods.

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Section: Literature Reviewmentioning

confidence: 99%

Learning Behavior Evaluation Model and Teaching Strategy Innovation by Social Media Network Following Learning Psychology

Yuan

et al. 2022

Front. Psychol.

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Discrete Dynamic Modeling Analysis Based on English Learning Motivation

Tang

Wang

Jiang

2022

Mathematical Problems in Engineering

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With the popularization of the Internet, various online learning platforms have developed rapidly, providing users with abundant learning resources, and realizing personalized resource recommendation has become the development trend of online learning platforms. In this paper, a personalized learning recommendation model based on improved collaborative filtering is proposed. Firstly, a multilayer interest model of learners is established to accurately describe learners’ interest in knowledge topics, courses, and knowledge areas; then, in view of the sparse scoring matrix and cold-start problems of traditional collaborative filtering recommendation algorithms, an improved collaborative filtering-based personality is proposed. The personalized learning recommendation model is used to improve the similarity calculation of users by introducing user initialization tags and solve the cold-start problem of new users. Finally, the effectiveness of the algorithm is proved by experimental comparison, and the improved algorithm improves the recommendation effect of personalized learning.

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A QoS prediction approach based on fusion of network representation learning and dynamic collaborative filtering for cloud service

Cited by 2 publications

References 0 publications

Learning Behavior Evaluation Model and Teaching Strategy Innovation by Social Media Network Following Learning Psychology

Learning Behavior Evaluation Model and Teaching Strategy Innovation by Social Media Network Following Learning Psychology

Discrete Dynamic Modeling Analysis Based on English Learning Motivation

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