Popularity Prediction of Tianya BBS Posts Based on User Behavior

2014 ◽  
pp. 33-43
Author(s):  
Ge Li ◽  
Yue Hu ◽  
Yanyu Yu
Keyword(s):  
User Behavior ◽  
Popularity Prediction

User Behavior Analysis and Video Popularity Prediction on a Large-Scale VoD System

2018 ◽  
Vol 14 (3s) ◽  
pp. 1-24 ◽  
Author(s):  
Lei Huang ◽  
Bowen Ding ◽  
Aining Wang ◽  
Yuedong Xu ◽  
Yipeng Zhou ◽  
...  
Keyword(s):  
Behavior Analysis ◽  
Large Scale ◽  
User Behavior ◽  
User Behavior Analysis ◽  
Popularity Prediction

Predictability of User Behavior in Social Media: Bottom-Up v Top-Down Modeling

2013 ◽  
Author(s):  
David Darmon ◽  
Jared Sylvester ◽  
Michelle Girvan ◽  
William M. Rand
Keyword(s):  
Social Media ◽  
User Behavior ◽  
Top Down ◽  
Bottom Up

A Survey on Improving the Web Search Ranking by User Behavior Information

2010 ◽  
Author(s):  
Mohamed Husain ◽  
Amarjeet Singh ◽  
Manoj Kumar ◽  
Rakesh Ranjan
Keyword(s):  
Web Search ◽  
User Behavior ◽  
The Web ◽  
Web Search Ranking

Asking costs little? The impact of tasks in video QoE studies on user behavior and user ratings

2013 ◽  
Author(s):  
Andreas Sackl ◽  
Michael Seufert ◽  
Tobias Hoßfeld
Keyword(s):  
User Behavior ◽  
User Ratings ◽  
The Impact

Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

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