EDMF: Efficient Deep Matrix Factorization With Review Feature Learning for Industrial Recommender System

Liu, Hai; Zheng, Chao; Li, Duantengchuan; Shen, Xiaoxuan; Lin, Kae‐Long; Wang, Jiazhang; Zhang, Zhen; Xiong, Neal N.

doi:10.1109/tii.2021.3128240

Cited by 166 publications

(34 citation statements)

References 30 publications

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“…The above characteristics make the PDIoT more vulnerable than traditional PDN. In recent years, the network security situation has become increasingly severe, and the security events of IoT and industrial control systems [1,2] have increased year by year [3,4]. Through analysis, we found that most of the security problems in PDIoT originated from the following sources: sensors, the network, and terminal devices.…”

Section: Introductionmentioning

confidence: 99%

Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

Cai

Wei

Deng

et al. 2022

Sensors

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The current power distribution Internet of Things (PDIoT) lacks security protection terminals and techniques. Network security has a large exposure surface that can be attacked from multiple paths. In addition, there are many network security vulnerabilities and weak security protection capabilities of power distribution Internet of Things terminals. Therefore, it is crucial to conduct a scientific assessment of the security of PDIoT. However, traditional security assessment methods are relatively subjective and ambiguous. To address the problems, we propose to use the entropy-weight method and cloud model theory to assess the security risk of the PDIoT. We first analyze the factors of security risks in PDIoT systems and establish a three-layer PDIoT security evaluation index system, including a perception layer, network layer, and application layer. The index system has three first-level indicators and sixteen second-level indicators. Then, the entropy-weight method is used to optimize the weight of each index. Additionally, the cloud model theory is employed to calculate the affiliation degree and eigenvalue of each evaluation index. Based on a comprehensive analysis of all evaluation indexes, we can achieve the security level of PDIoT. Taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example for empirical testing, the experimental results show that the evaluation results are consistent with the actual situation, which proves that the proposed method is effective and feasible.

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Section: Introductionmentioning

confidence: 99%

Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

Cai

Wei

Deng

et al. 2022

Sensors

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“…While Tempotron is not the latest architecture, its various applications and enhancements are carried out by numerous research groups even nowadays [18][19][20]. Such reusability of classical methods can be observed in the field of artificial intelligence in general [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Effects of Caputo Fractional Derivative in Spiking Neural Network Training

Gyongyossy

Eros²,

Botzheim³

2022

Electronics

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Fractional calculus is an emerging topic in artificial neural network training, especially when using gradient-based methods. This paper brings the idea of fractional derivatives to spiking neural network training using Caputo derivative-based gradient calculation. We focus on conducting an extensive investigation of performance improvements via a case study of small-scale networks using derivative orders in the unit interval. With particle swarm optimization we provide an example of handling the derivative order as an optimizable hyperparameter to find viable values for it. Using multiple benchmark datasets we empirically show that there is no single generally optimal derivative order, rather this value is data-dependent. However, statistics show that a range of derivative orders can be determined where the Caputo derivative outperforms first-order gradient descent with high confidence. Improvements in convergence speed and training time are also examined and explained by the reformulation of the Caputo derivative-based training as an adaptive weight normalization technique.

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“…Different from a traditional entity-typing task that typically classifies entities into coarse-grained types (e.g., person, location, organization ) [ 3 , 4 ], FET aims to assign an entity with more specific types [ 5 , 6 ], which usually follow a hierarchical structure that can provide more semantic information about the entity [ 7 , 8 ], such as /person/politician , /book/author , etc. FET is a significant subtask of named-entity recognition (NER) [ 9 ] for downstream natural language processing (NLP) applications, such as relation extraction [ 10 , 11 ], question answering [ 12 , 13 ], knowledge base population [ 14 ], and recommendation [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks

Zhang

Han

et al. 2022

Entropy

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Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. However, most existing methods neglect rich known typing information about these entities in knowledge graphs. To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. Specifically, we propose a novel deep neural model called CopyFet for FET via a copy-generation mechanism. CopyFet can integrate two operations: (i) the regular way of making type inference from the whole type set in the generation model; (ii) the new copy mechanism which can identify the semantic type of a mention with reference to the type-copying vocabulary from a knowledge graph in the copy model. Despite its simplicity, this mechanism proves to be powerful since extensive experiments show that CopyFet outperforms state-of-the-art methods in FET on two benchmark datasets (FIGER (GOLD) and BBN). For example, CopyFet achieves the new state-of-the-art score of 76.4% and 83.6% on the accuracy metric in FIGER (GOLD) and BBN, respectively.

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EDMF: Efficient Deep Matrix Factorization With Review Feature Learning for Industrial Recommender System

Cited by 166 publications

References 30 publications

Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

Security Risk Intelligent Assessment of Power Distribution Internet of Things via Entropy-Weight Method and Cloud Model

Exploring the Effects of Caputo Fractional Derivative in Spiking Neural Network Training

Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks

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