User-Interest-Based Document Filtering via Semi-supervised Clustering

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

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Microblog User Interest Recognition Based on Multi-Granularity Text Feature Representation

The 2nd International Conference on Computing and Data Science ◽

10.1145/3448734.3450886 ◽

2021 ◽

Author(s):

Youqin Yu ◽

Bicheng Li

Keyword(s):

Feature Representation ◽

User Interest ◽

Text Feature

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Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Vibration ◽

10.3390/vibration4010004 ◽

2020 ◽

Vol 4 (1) ◽

pp. 49-63

Author(s):

Waad Subber ◽

Sayan Ghosh ◽

Piyush Pandita ◽

Yiming Zhang ◽

Liping Wang

Keyword(s):

Dynamical Systems ◽

Computational Cost ◽

Three Dimensional ◽

Region Of Interest ◽

System Response ◽

Computational Domain ◽

User Interest ◽

Numerical Resolution ◽

Multi Scale ◽

Operation Conditions

Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded physics. In practice however, high numerical resolution is only required in a confined region of the domain where fast dynamics or localized material variability is exhibited, whereas a coarser discretization can be sufficient in the rest majority of the domain. Partitioning the complex dynamical system into smaller easier-to-solve problems based on the localized dynamics and material variability can reduce the overall computational cost. The region of interest can be specified based on the localized features of the solution, user interest, and correlation length of the material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update the prior knowledge of the localized region of interest using measurements of the system response. Once, the region of interest is identified, the localized uncertainty is propagate forward through the computational domain. We demonstrate our framework using numerical experiments on a three-dimensional elastodynamic problem.

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