Pre-Occupancy Evaluation based on user behavior prediction in 3D virtual simulation

2017 ◽  
Vol 74 ◽  
pp. 55-65 ◽  
Author(s):  
Sangyun Shin ◽  
Sangah Jeong ◽  
Jaewook Lee ◽  
Seung Wan Hong ◽  
Sungwon Jung
Keyword(s):  
User Behavior ◽  
Virtual Simulation ◽  
Behavior Prediction

Data-driven User Behavior Prediction Based on Integrated Prediction Model

2021 ◽  
Author(s):  
Xiangyu Zhang ◽  
Jun Fang ◽  
Jingfan Zou ◽  
Wenfang Li ◽  
Weigang Xu ◽  
...  
Keyword(s):  
Prediction Model ◽  
User Behavior ◽  
Data Driven ◽  
Behavior Prediction

Virtual Simulation for Clutch Thermal Behavior Prediction

2018 ◽  
Author(s):  
Fabio Tosi ◽  
Matteo Gautero ◽  
Laura Lorefice ◽  
Nicola Paola lng
Keyword(s):  
Thermal Behavior ◽  
Virtual Simulation ◽  
Behavior Prediction

scholarly journals LAR: A User Behavior Prediction Model in Server Log Based on LSTM-Attention Network and RSC Algorithm

2020 ◽  
Author(s):  
Yingying Shang
Keyword(s):  
Clustering Algorithm ◽  
Short Term Memory ◽  
User Behavior ◽  
Absolute Error ◽  
Research Area ◽  
Behavior Prediction ◽  
Attention Network ◽  
Important Research Area ◽  
User Access ◽  
Single User

Using server log data to predict the URLs that a user is likely to visit is an important research area in user behavior prediction. In this paper, a predictive model (called LAR) based on the long short-term memory (LSTM) attention network and reciprocal-nearest-neighbors supported clustering algorithm (RSC) for predicting the URL is proposed. First, the LSTM-attention network is used to predict the URL categories a user might visit, and the RSC algorithm is then used to cluster users. Subsequently, the URLs belonging to the same category are determined from the user clusters to predict the URLs that the user might visit. The proposed LAR model considers the time sequence of the user access URL, and the relationship between a single user and group users, which effectively improves the prediction accuracy. The experimental results demonstrate that the LAR model is feasible and effective for user behavior prediction. The accuracy of the mean absolute error and root mean square error of the LAR model are better than those of the other models compared in this study.

User behavior prediction via heterogeneous information preserving network embedding

2019 ◽  
Vol 92 ◽  
pp. 52-58 ◽  
Author(s):  
Weiwei Yuan ◽  
Kangya He ◽  
Guangjie Han ◽  
Donghai Guan ◽  
Asad Masood Khattak
Keyword(s):  
User Behavior ◽  
Behavior Prediction ◽  
Network Embedding ◽  
Heterogeneous Information

Cluster-Based Smartphone Predictive Analytics for Application Usage and Next Location Prediction

2018 ◽  
Vol 9 (2) ◽  
pp. 64-80
Author(s):  
Xiaoling Lu ◽  
Bharatendra Rai ◽  
Yan Zhong ◽  
Yuzhu Li
Keyword(s):  
Prediction Models ◽  
Predictive Analytics ◽  
User Behavior ◽  
Training Dataset ◽  
Location Prediction ◽  
Behavior Prediction ◽  
Test Dataset ◽  
Smartphone Sensors ◽  
Using Data ◽  
Prediction Approach

Prediction of app usage and location of smartphone users is an interesting problem and active area of research. Several smartphone sensors such as GPS, accelerometer, gyroscope, microphone, camera and Bluetooth make it easier to capture user behavior data and use it for appropriate analysis. However, differences in user behavior and increasing number of apps have made such prediction a challenging problem. In this article, a prediction approach that takes smartphone user behavior into consideration is proposed. The proposed approach is illustrated using data from over 30000 users from a leading IT company in China by first converting data in to recency, frequency, and monetary variables and then performing cluster analysis to capture user behavior. Prediction models are then developed for each cluster using a training dataset and their performance is assessed using a test dataset. The study involves ten different categories of apps and four different regions in Beijing. The proposed app usage prediction and next location prediction approach has provided interesting results.

scholarly journals Action Space Learning for Heterogeneous User Behavior Prediction

2019 ◽  
Author(s):  
Dongha Lee ◽  
Chanyoung Park ◽  
Hyunjun Ju ◽  
Junyoung Hwang ◽  
Hwanjo Yu
Keyword(s):  
User Behavior ◽  
Metric Learning ◽  
Action Space ◽  
Single Type ◽  
Behavior Prediction ◽  
Web Based ◽  
User Behaviors ◽  
Speed Up ◽  
High Level ◽  
User Actions

Users' behaviors observed in many web-based applications are usually heterogeneous, so modeling their behaviors considering the interplay among multiple types of actions is important. However, recent collaborative filtering (CF) methods based on a metric learning approach cannot learn multiple types of user actions, because they are developed for only a single type of user actions. This paper proposes a novel metric learning method, called METAS, to jointly model heterogeneous user behaviors. Specifically, it learns two distinct spaces: 1) action space which captures the relations among all observed and unobserved actions, and 2) entity space which captures high-level similarities among users and among items. Each action vector in the action space is computed using a non-linear function and its corresponding entity vectors in the entity space. In addition, METAS adopts an efficient triplet mining algorithm to effectively speed up the convergence of metric learning. Experimental results show that METAS outperforms the state-of-the-art methods in predicting users' heterogeneous actions, and its entity space represents the user-user and item-item similarities more clearly than the space trained by the other methods.

Sign in / Sign up

Export Citation Format

Share Document