Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier

Xiao, Guangnian; Cheng, Qiuming; Zhang, Chunqin

doi:10.1109/access.2019.2936443

Cited by 58 publications

(40 citation statements)

References 42 publications

(46 reference statements)

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“…e introduction of the probability model has greatly improved the performance of matrix factorization and further improved the accuracy of the matrix factorization model [28]. Probability matrix decomposition has two leading assumptions: one is that the difference between the overall rating matrix R of the user and the inner product R of the eigenvectors of the user and the movie obeys the Gaussian distribution of variance [29]; the second is that the eigenvector matrix U of the user and the movie's elements of the eigenvector matrix V, respectively, obey the Gaussian distribution with the mean value being 0 and the variance being ϕ u and ϕ v [30,31].…”

Section: Matrix Decomposition Recommendation Algorithmmentioning

confidence: 99%

Personalized Movie Recommendation Method Based on Deep Learning

Liu

Choi

2021

Mathematical Problems in Engineering

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With the rapid development of network technology and entertainment creation, the types of movies have become more and more diverse, which makes users wonder how to choose the type of movies. In order to improve the selection efficiency, recommend Algorithm came into being. Deep learning is a research field that has received extensive attention from scholars in recent years. Due to the characteristics of its deep architecture, deep learning models can learn more complex structures. Therefore, deep learning algorithms in speech recognition, machine translation, image recognition, and other fields have achieved impressive results. This article mainly introduces the research of personalized movie recommendation methods based on deep learning and intends to provide ideas and directions for the research of personalized movie recommendation under deep learning. This paper proposes a research method of personalized movie recommendation methods based on deep learning, including an overview of personalized recommendation and collaborative filtering recommendation algorithms, which are used to conduct research experiments on personalized movie recommendation methods based on deep learning. The experimental results in this paper show that the accuracy of the training set of the Seq2Seq model based on the LSTM recurrent neural network reaches 96.27% and the accuracy of the test set reaches 95.89%, which can be better for personalized movie recommendation.

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Section: Matrix Decomposition Recommendation Algorithmmentioning

confidence: 99%

Personalized Movie Recommendation Method Based on Deep Learning

Liu

Choi

2021

Mathematical Problems in Engineering

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“…Assuming that the potential variable is a polynomial, we can construct a Gaussian mixture model [13].…”

Section: Cluster Regression Analysis Modelmentioning

confidence: 99%

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Jin

Yi²

2021

Mathematical Problems in Engineering

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Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

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“…The platform is named as "SurveyStar" in English and operated by Changsha Ranxing Science and Technology Ltd. In comparison with the traditional paper-based questionnaire survey, the online survey has many advantages, including online data verification, fast collection, and avoidance of incorrect manual type-in [44,45]. Tables 2 and 3.…”

Section: Descriptive Statistical Resultsmentioning

confidence: 99%

Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era

Xiao

Wang

2020

Sustainability

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With the rapid popularization of mobile Internet technology and smart terminal equipment in recent years, the volume and usage of dockless bikesharing (hereafter referred to as bikesharing), which is green, environmentally friendly and convenient, have grown rapidly, making it one of the China’s “new four major inventions.” The development of the bikesharing in China consists of a pre-sharing era and a post-sharing era. In the pre-sharing era, capital-driven vicious market competition and lack of precise control have led to the abuse of urban space. Since the post-sharing era, the industry structure has returned to rationality, and many participants have been forced out of the market. The bikesharing has formed an oligopoly market consisting of head players such as Hellobike, Mobike, and Ofo. Therefore, how to improve the level of refined operations, promote sustainable development, improve cyclist satisfaction, and contribute to China’s strength in transportation have become urgent problems for bikesharing companies and traffic management departments. From the perspective of the cyclist experience, the brand choice of the bikesharing is taken as the research object. An online revealed preference survey is used to collect data on cyclists’ socio-economic attributes and subjective evaluations on the bikesharing. The conditional Logit model is used to explore the important factors that influence cyclists on the choice of bikesharing brands. Research results include: (1) age, occupation type, after-tax monthly income of the faculty group, riding comfort, rent, picking up/returning convenience, word of mouth, and volume have a significant impact on cyclists’ bikesharing brand choices; (2) gender, educational background, monthly living expenses of the student group, appearance, deposit, deposit returning speed, rate of broken bikes, ease of use of software, and rent discount have no significant impact on cyclists’ bikesharing brand choices. The research results are of great significance for improving the service quality of bikesharing companies and promoting the healthy development of the shared economy in China. Based on the results of the study, policy recommendations are made on the improvement for riding comfort, human-centered design, and word of mouth, and the construction of shared facilities.

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Detecting Travel Modes Using Rule-Based Classification System and Gaussian Process Classifier

Cited by 58 publications

References 42 publications

Personalized Movie Recommendation Method Based on Deep Learning

Personalized Movie Recommendation Method Based on Deep Learning

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era

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