Research on recommender algorithm optimization based on statistics and preference model

Author(s):  
Jia Wang ◽  
Xia Song ◽  
Qibing Jin ◽  
Dan Song
2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.


2007 ◽  
Vol 13 (1s) ◽  
pp. 33-37
Author(s):  
V. Makarenko ◽  
◽  
G. Ruecker ◽  
R. Sommer ◽  
N. Djanibekov ◽  
...  

2021 ◽  
pp. 096739112110233
Author(s):  
Mohammad Hassan Shojaeefard ◽  
Abolfazl Khalkhali ◽  
Sharif Khakshournia

It has been demonstrated that adding a few percent of nanoscale reinforcements, leads to remarkable improvement in mechanical properties of the polymers such as stiffness, damping, and energy absorption. These lightweight materials are attractive substitutes for the heavy metallic structural parts in the automotive, military, aerospace and many other industries. However, due to complexity of these multiphase materials, accurate modeling of their behavior in real loading cases is still ambiguous. The impact simulation is a vital step in design procedure of a vehicle, where a strain rate-dependent model of its components is required. In this paper, an elasto-viscoplastic modeling procedure of the polymer-based nanocomposites, assuming the elastic behavior of the nano-phase is presented; whereas the polymeric matrix deformation is dependent to the loading rate and is characterized by the method of Genetic algorithm optimization-based fitting to the experimental observations. By introducing a modified Halpin-Tsai method, the nanocomposite is then modeled as a homogenized material where the modification algorithm is the main challenge. A combination of approaches including parametric analysis, central composite design of experiments and response surface method is proposed to modify the tangent modulus of the polymeric matrix to be passed as the input to the Halpin-Tsai equations. Finally, the procedure is implemented to a set of epoxy-GNP nanocomposites under unidirectional compressive loads with different rates and the stress-strain curves are predicted with a decent precision.


2021 ◽  
Vol 118 (23) ◽  
pp. 234001
Author(s):  
Yun Chen ◽  
Chengyuan Wang ◽  
Ya Yu ◽  
Zibin Jiang ◽  
Jinwen Wang ◽  
...  

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