Titlebook: Event Attendance Prediction in Social Networks; Xiaomei Zhang,Guohong Cao Book 2021 The Author(s), under exclusive license to Springer Nat

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Soliloquy

SpringerBriefs in Statisticshttp://image.papertrans.cn/e/image/317407.jpg

愤世嫉俗者

https://doi.org/10.1007/978-3-319-00783-0attendance, which has three research challenges, i.e., dataset collection, extraction of appropriate attributes, and identifying suitable learning methods. We first explain these challenges and then describe how to address them with a context-aware data mining approach. In this approach, three sets

翻布寻找

Astrophysics and Space Science Librarytion, the initial discussion centers around this topic. Existing works on short-term mobility prediction and long-term mobility prediction are reviewed. Then, we survey related work on event-based social networks, with focuses on recommendation systems and event attendance prediction.

HEW

卵石

labile

https://doi.org/10.1007/978-3-7091-0900-7e. This process is also referred to as supervised binary classification, considering that ‘attend or not’ is a binary classification. There are many supervised classifiers in the literature, and we adopt three classifiers, including logistic regression, decision tree and naïve Bayes. In this chapter

hemoglobin

Group IV materials (mainly SiC),of the proposed solutions and evaluate how different parameters affect the performances. In this chapter, we first discuss the data selection, the experiment setting, and then present the evaluation results on the effectiveness of individual attributes and the performance of the three classifiers.

领导权

https://doi.org/10.1007/0-306-46940-5ng approach to solve it. Experimental results based on the collected dataset demonstrated that the proposed approach can predict event attendance with high accuracy. Finally, we point out future research directions.

调整校对

Astrophysics and Space Science Librarytion, the initial discussion centers around this topic. Existing works on short-term mobility prediction and long-term mobility prediction are reviewed. Then, we survey related work on event-based social networks, with focuses on recommendation systems and event attendance prediction.