A novel cascade hybrid many-objective recommendation algorithm incorporating multistakeholder concerns

Wang, Dandan; Chen, Yan

doi:10.1016/j.ins.2021.07.005

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…The research constructs a new provider visibility model through provider coverage, user arrival coverage, and provider entropy. Experimental results showed that the proposed algorithm could effectively upgrade unbalanced provider recommendations and reduce the complexity of high-dimensional multi-target recommendations [18].…”

Section: Related Workmentioning

confidence: 99%

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

Li¹,

Yin²,

Hui³

2023

Computers, Materials &Amp; Continua

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With the rapid development of the Internet globally since the 21st century, the amount of data information has increased exponentially. Data helps improve people's livelihood and working conditions, as well as learning efficiency. Therefore, data extraction, analysis, and processing have become a hot issue for people from all walks of life. Traditional recommendation algorithm still has some problems, such as inaccuracy, less diversity, and low performance. To solve these problems and improve the accuracy and variety of the recommendation algorithms, the research combines the convolutional neural networks (CNN) and the attention model to design a recommendation algorithm based on the neural network framework. Through the text convolutional network, the input layer in CNN has transformed into two channels: static ones and non-static ones. Meanwhile, the self-attention system focuses on the system so that data can be better processed and the accuracy of feature extraction becomes higher. The recommendation algorithm combines CNN and attention system and divides the embedding layer into user information feature embedding and data name feature extraction embedding. It obtains data name features through a convolution kernel. Finally, the top pooling layer obtains the length vector. The attention system layer obtains the characteristics of the data type. Experimental results show that the proposed recommendation algorithm that combines CNN and the attention system can perform better in data extraction than the traditional CNN algorithm and other recommendation algorithms that are popular at the present stage. The proposed algorithm shows excellent accuracy and robustness.

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Section: Related Workmentioning

confidence: 99%

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

Li¹,

Yin²,

Hui³

2023

Computers, Materials &Amp; Continua

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show abstract

“…43 An optimization problem whose objective function to be optimized exceeds three is regarded as a many-objective optimization problem (MaOP). 44 Such problems are very common in reality, especially in the fields of the Internet of Things, [45][46][47] recommendation systems, [48][49][50][51] and engineering production. 52,53 As the quantity of objectives rises, population diversity and convergence cannot be guaranteed by using multi-objective evolutionary algorithms to solve MaOPs.…”

Section: Many-objective Optimizationmentioning

confidence: 99%

A many‐objective optimization based intelligent algorithm for virtual machine migration in mobile edge computing

Fan

Guo

Zhang

et al. 2023

Concurrency and Computation

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SummaryWith the rapid development of big data, the explosive growth of data promotes the progress of the Internet of Things (IoT). Because it is hard for traditional cloud computing to meet vast computing tasks, scholars propose mobile edge computing (MEC) for the IoT. However, the mobility of users results in the instability of MEC performance. Besides, the conflict of interest between users and service providers needs to be balanced. To solve these problems, this paper constructs a virtual machine migration model based on many‐objective optimization (MaOVMMM). In MaOVMMM, four objectives are considered simultaneously: communication expense, computing expense, delay, and energy consumption. A many‐objective evolutionary algorithm with double population confrontation (MaOEA‐DPC) is suggested to support the MaOVMMM that is proposed. First, the population confrontation strategy is designed to better simulate the relationship between users and service providers. Second, the dynamic probability integration selection strategy is used to ensure the evolution ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of MaOEA‐DPC when compared with other algorithms. This proposed approach can provide a superior virtual machine migration scheme for decision‐makers.

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“…3389/fnins.2022.984404 is the cold-start or long-tail effect (Roy and Guntuku, 2016). However, other research directions of recommendation systems are not involved, such as multiobjective recommender systems (MORS) (Wang and Chen, 2021) or multi-task recommender systems (MTRS) (Ma et al, 2018) and explainable recommender systems (ERS) (Zhang and Chen, 2020). The MORS or MTRS can incorporate more objectives or tasks into the video recommendation based on affective computing; these models focus on more extensive aspects of recommendation quality, such as diversity, novelty, etc.…”

Section: Related Research Papersmentioning

confidence: 99%

Affective video recommender systems: A survey

Wang

Zhao

2022

Front. Neurosci.

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Traditional video recommendation provides the viewers with customized media content according to their historical records (e.g., ratings, reviews). However, such systems tend to generate terrible results if the data is insufficient, which leads to a cold-start problem. An affective video recommender system (AVRS) is a multidiscipline and multimodal human-robot interaction (HRI) system, and it incorporates physical, physiological, neuroscience, and computer science subjects and multimedia resources, including text, audio, and video. As a promising research domain, AVRS employs advanced affective analysis technologies in video resources; therefore, it can solve the cold-start problem. In AVRS, the viewers’ emotional responses can be obtained from various techniques, including physical signals (e.g., facial expression, gestures, and speech) and internal signals (e.g., physiological signals). The changes in these signals can be detected when the viewers face specific situations. The physiological signals are a response to central and autonomic nervous systems and are mostly involuntarily activated, which cannot be easily controlled. Therefore, it is suitable for reliable emotion analysis. The physical signals can be recorded by a webcam or recorder. In contrast, the physiological signals can be collected by various equipment, e.g., psychophysiological heart rate (HR) signals calculated by echocardiogram (ECG), electro-dermal activity (EDA), and brain activity (GA) from electroencephalography (EEG) signals, skin conductance response (SCR) by a galvanic skin response (GSR), and photoplethysmography (PPG) estimating users’ pulse. This survey aims to provide a comprehensive overview of the AVRS domain. To analyze the recent efforts in the field of affective video recommendation, we collected 92 relevant published articles from Google Scholar and summarized the articles and their key findings. In this survey, we feature these articles concerning AVRS from different perspectives, including various traditional recommendation algorithms and advanced deep learning-based algorithms, the commonly used affective video recommendation databases, audience response categories, and evaluation methods. Finally, we conclude the challenge of AVRS and provide the potential future research directions.

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A novel cascade hybrid many-objective recommendation algorithm incorporating multistakeholder concerns

Cited by 14 publications

References 32 publications

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

Recommendation Algorithm Integrating CNN and Attention System in Data Extraction

A many‐objective optimization based intelligent algorithm for virtual machine migration in mobile edge computing

Affective video recommender systems: A survey

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