Effect of channel quality estimation error on the performance of interactive mobile video system

2002 ◽  
Author(s):  
Fuyu Long ◽  
Kwok-Tung Lo ◽  
Wan-Chi Siu ◽  
Fulmi Long
Keyword(s):  
Estimation Error ◽  
Mobile Video ◽  
Quality Estimation ◽  
Video System ◽  
Channel Quality

Mobile Sports Video with Total Users Control

2007 ◽  
pp. 596-604
Author(s):  
D. Tjondronegoro
Keyword(s):  
Mobile Devices ◽  
Mobile Video ◽  
Current Generation ◽  
Video System ◽  
Sports Video ◽  
Video Representation ◽  
Full Support ◽  
Non Linear ◽  
Total Control ◽  
Video Services

Sports video is very popular thanks to its in-progress (live) information and entertainment values. Many users are motivated to access sports video using mobile devices, since they often cannot watch the game on their sofa due to a busy life and inability to cope with lengthy games. The current generation of mobile video services has only focused on supporting the when and where consumers can watch their favorite sports matches. Since total control over playback and content is neglected, users often have to settle with low-quality videos and static content, which have been pre-processed. This limitation slows down the progress towards an era in which users are comfortable using their mobile devices to enjoy sports broadcasts while gaining total control over what they can watch at their most convenient time and place. In this article, we will describe a mobile video system which offers users full support over the when, where and how they want to watch sports video. The main new features offered are: (1) non-linear navigation within single and/or multiple documents; (2) customizable and personalized summaries; (3) multimodal access and video representation.

scholarly journals Understanding Persuasion Cascades in Online Product Rating Systems

2019 ◽  
Vol 33 ◽  
pp. 5490-5497
Author(s):  
Hong Xie ◽  
Yongkun Li ◽  
John C.S. Lui
Keyword(s):  
Product Quality ◽  
Estimation Error ◽  
Opinion Dynamics ◽  
Estimation Accuracy ◽  
Matrix Perturbation ◽  
Rating Systems ◽  
Quality Estimation ◽  
Scoring Rule ◽  
Product Ratings ◽  
Product Rating

Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google play store and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called “messagebased persuasion” lead to “biased” product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.

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