A Parallel Recommender System Using a Collaborative Filtering Algorithm with Correntropy for Social Networks

Sun, Jiankun; Wang, Zi-Yang; Luo, Xiong; Shi, Peng; Wang, Weiping; Wang, Long; Wang, Long; Zhao, Wenbing

doi:10.1109/tnse.2018.2862948

Cited by 20 publications

(4 citation statements)

References 56 publications

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“…Furthermore, motivated by some successful work [ 46 , 47 , 48 , 49 ], we introduce the parallel structure into the proposed algorithm to make full use of computing resources and reduce the running time. Generally, parallel models of the common GA can be divided into four categories, including the master-slave model, coarse-grained model, fine-grained model, and hybrid model [ 47 ].…”

Section: The Proposed Solution Methods Using the Pa-jaya Algorithmmentioning

confidence: 99%

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Luo

Wang

et al. 2018

Sensors

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Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate.

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Section: The Proposed Solution Methods Using the Pa-jaya Algorithmmentioning

confidence: 99%

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Luo

Wang

et al. 2018

Sensors

Self Cite

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“…Item-based Collaborative Filtering (IBCF) is another strategy based on favorite items of a given user. Similar to UBCF, it suffers from the dispersion of rates and stagnation, which is defined as the system's failure to recommend recently added items until some users rate them [33].…”

Section: Content-based Filtering (Cbf)mentioning

confidence: 99%

Explainable AI in big data intelligence of community detection for digitalization e-healthcare services

Sangaiah

Rezaei

Javadpour

et al. 2023

Applied Soft Computing

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“…Collaborative filtering mainly has two types: user-based and item-based [ [5]]. Userbased algorithms recommend items that users with similar interests have liked but the target user has not purchased, while item-based algorithms recommend items similar to those that the target user likes [ [6]].…”

Section: Introductionmentioning

confidence: 99%

The News Delivery Channel Recommendation Based on Granular Neural Network

Liu

2023

Preprint

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With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many re-al-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various plat-forms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources.

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A Parallel Recommender System Using a Collaborative Filtering Algorithm with Correntropy for Social Networks

Cited by 20 publications

References 56 publications

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

Explainable AI in big data intelligence of community detection for digitalization e-healthcare services

The News Delivery Channel Recommendation Based on Granular Neural Network

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