DP-BPR: Destination prediction based on Bayesian personalized ranking

Jiang, Feng; Lu, Zhenni; Gao, Min; Luo, Daming

doi:10.1007/s11771-021-4617-x

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…In a new book on curriculum, some experts state that teaching evaluation is a process of measuring and judging the evaluation objects in teaching activities according to certain standards. On the basis of objective teaching practice, here, the concept of teaching evaluation is defined as the process of judging the value of teaching work with scientific and reasonable methods [18].…”

Section: Teaching Evaluation and Pe Teaching Evaluationmentioning

confidence: 99%

[Retracted] Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

2021

Journal of Sensors

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The purpose is to enrich the evaluation system of physical education (PE) teaching in colleges and universities and to improve PE teaching methods and improve teaching quality. Based on big data information fusion and data mining technology, firstly, the related theories of teaching evaluation are analyzed and expounded, as well as the characteristics and principles of the construction of college PE teaching evaluation system. Secondly, from the perspective of evaluation index system of sports teachers’ teaching and students’ sports teaching, the content and evaluation index of college sports teaching evaluation are analyzed under the background of big data information fusion and data mining by questionnaire survey. Combined with model test, the results show that traditional college sports teacher pays more attention to the design and teaching methods of PE and ignore the learning process of students. The evaluation process of PE ignores the individual differences of students, the feedback method lacks openness, and the evaluation process is isolated. Based on the big data technology and teaching evaluation theory, the evaluation index is designed for PE teaching in colleges and universities. The average value of the first layer indexes is above 4, and the coefficient of variation is less than 0.2, which can basically reflect the content of PE teaching evaluation and provide some reference for the research of PE teaching evaluation.

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Section: Teaching Evaluation and Pe Teaching Evaluationmentioning

confidence: 99%

[Retracted] Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

2021

Journal of Sensors

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“…First, we found that the method used in some studies like Zhao et al [2018] does not have the ability to predict destinations where individuals lack observations, but the method still works well experimentally under its data condition. Secondly, in analysing the comparative methods studied, we found that very simple regularity-based methods can also achieve good results under their data conditions such as Jiang et al [2021]. The above information indicates that under the existing studies' data conditions, the destination or trajectory is almost completely observed and the regularity of the data is exposed.…”

Section: Literature Reviewmentioning

confidence: 83%

Potential destination prediction for low predictability individuals based on knowledge graph

Li¹,

Chen²,

Liao³

et al. 2022

Preprint

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Destination prediction has been a critical topic in transportation research, and there are a large number of studies. However, almost all existing studies are based on high predictability data conditions while pay less attention to the data condition with low predictability, where the regularity of single individuals is not exposed. Based on a certain period of observation, there is a fact that individuals may choose destinations beyond observation, which we call "potential destinations". The number of potential destinations is very large and can't be ignored for the data condition with low predictability formed by short-term observation. To reveal the choice pattern of potential destination of individuals under the data condition with low predictability, we propose a global optimization method based on knowledge graph embedding. First, we joint the trip data of all individuals by constructing Trip Knowledge Graph(TKG). Next, we optimize the general algorithm of knowledge graph embedding for our data and task in training strategy and objective function, then implement it on TKG. It can achieve global optimization for association paths that exist between almost any two entities in TKG. On this basis, a method for potential destination prediction is proposed, giving the possible ranking of unobserved destinations for each individual. In addition, we improve the performance by fusing static statistical information that is not passed to TKG. Finally, we validate our method in a real-world dataset, and the prediction results are highly consistent with individuals' potential destination choice behaviour.

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“…The synthetic individual-level vehicle trip dataset has a wide range of use for research, such as studies focusing on travellers’ trip behaviour pattern 18 , trip prediction 17 , 19 , 20 , travel time estimation 10 , 21 , origin-destination pattern estimation 16 and analysis of the effect of transportation policies 22 . Also, this dataset can support studies using aggregated data like traffic volume 23 , 24 .…”

Section: Background and Summarymentioning

confidence: 99%

City-scale synthetic individual-level vehicle trip data

Chen

Wang

et al. 2023

Sci Data

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Trip data that records each vehicle’s trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.

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DP-BPR: Destination prediction based on Bayesian personalized ranking

Cited by 8 publications

References 22 publications

[Retracted] Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

[Retracted] Facing Big Data Information Fusion and Data Mining Technology to Construct College Physical Education Teaching Evaluation System

Potential destination prediction for low predictability individuals based on knowledge graph

City-scale synthetic individual-level vehicle trip data

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