A Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videos

Tan, Zhiyi; Wang, Yanfeng; Zhang, Ya; Zhou, Jun

doi:10.1109/tbc.2016.2540522

Cited by 28 publications

(16 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some prediction based on the persistence dynamics of trends in online social interaction. Tan et al [245] treated the popularity of online videos as time series over the given periods and proposed a novel time series model for popularity prediction. The proposed model is based on the correlation between early and future popularity series.…”

Section: Trend Prediction In Online Social Interactionsmentioning

confidence: 99%

Structure-oriented prediction in complex networks

2018

View full text Add to dashboard Cite

Complex systems are extremely hard to predict due to its highly nonlinear interactions and rich emergent properties. Thanks to the rapid development of network science, our understanding of the structure of real complex systems and the dynamics on them has been remarkably deepened, which meanwhile largely stimulates the growth of effective prediction approaches on these systems. In this article, we aim to review different networkrelated prediction problems, summarize and classify relevant prediction methods, analyze their advantages and disadvantages, and point out the forefront as well as critical challenges of the field. Contents

show abstract

Section: Trend Prediction In Online Social Interactionsmentioning

confidence: 99%

Structure-oriented prediction in complex networks

2018

View full text Add to dashboard Cite

show abstract

“…To understand the concentration of video popularity in the spatial dimension of the city, we calculate the average number of views of top 30% topics by each user according to (4) in the large scale and plot the results in Fig. 12.…”

Section: A Concentration Of Video Popularitymentioning

confidence: 99%

“…To deal with it, the analysis based on real-world dataset, as an important direction understanding human behaviors in multimedia systems, can be also applied to characterize the patterns of video views since it reflects realistic information of video consumption. Many recent works collect viewing data from CPs to study the popularity of videos over time [12], [13], and some of them utilize the temporal feature to predict the video popularity [4]. Unlike them, in our study, we focus on the spatial characteristics of watching videos, which is greatly important for networking, i.e., it is critical for CPs to design the effective content distributed systems to put more popular videos close to users for better viewing experience.…”

Section: Introductionmentioning

confidence: 99%

Spatial Popularity and Similarity of Watching Videos in Large-Scale Urban Environment

Yan

Liu

et al. 2018

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

With the popularity of watching mobile videos, a major form of multimedia content, many works focus on the geographic features of user viewing behaviors, but few study them in the context of an entire metropolitan city. Different regions of a large city have different intensity of economy activities with respect to their different distances to the downtown, and how this will influence video popularity and similarity is still unclear. To quantitatively study the spatial popularity and similarity of watching videos in a large urban environment, we collect a dataset with two-month video view requests from the largest network provider in Shanghai, containing top six content providers, and study the spatial features of video access in regions of different scales. We find that 1) video popularity and similarity exist at different scales of city division; 2) the concentration of video popularity becomes higher as the region is closer to downtown; 3) when comparing the regions of same scale, the similarity of popular videos becomes lower as the region is farther away from the downtown. Finally, we correlate our findings with cache deployment, advertising and video recommendation to illustrate the implications.

show abstract

“…Time-series methods use the historical content requests to predict the future content requests. Multivariate linear • W. Hoiles models [1], pure birth stochastic process [2], and ordinary differential equations [3] have all been used for constructing time-series methods for estimating content requests. A limitation with time-series methods is that they can only be used to predict the content requests of posted content.…”

Section: Introductionmentioning

confidence: 99%

“…The system model of the femtocell network is provided in Sec. 2 where Table 1 provides a summary of the parameters used throughout the paper. In Sec.3 dynamic cache replacement policies are discussed.…”

Section: Introductionmentioning

confidence: 99%

Risk-Averse Caching Policies for YouTube Content in Femtocell Networks using Density Forecasting

Hoiles

Tanzil

Krishnamurthy

2021

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

The paper presents risk-neutral and risk-averse caching policies that can be deployed in a femtocell network with limited storage capacity to reduce the time delay of servicing content requests. The caching policies use a forecasting algorithm to estimate the cumulative distribution function of content requests based on the content features. Given the cumulative distribution function, a mixed-integer linear program is used to compute where to cache content in the femtocell network. The caching policies account for the uncertainty associated with estimating the content requests using the coherent Conditional Value-at-Risk (CVaR) measure. For a large number of content, a risk-neutral caching policy is constructed that accounts for both the content features and routing protocol that only requires the evaluation of a unimodular linear program. Using data from YouTube (comprising 25,000 videos) and the NS-3 simulator, the caching policies reduce the delay of retrieving content in femtocell networks compared with industry standard caching policies. Specifically, a 6% reduction in delay is achieved by accounting for the uncertainty, and a 60% reduction in delay is achieved if both the uncertainty and femtocell routing protocol are accounted for compared to the risk-neutral caching policy that neglects the routing protocol.

show abstract

A Novel Time Series Approach for Predicting the Long-Term Popularity of Online Videos

Cited by 28 publications

References 23 publications

Structure-oriented prediction in complex networks

Structure-oriented prediction in complex networks

Spatial Popularity and Similarity of Watching Videos in Large-Scale Urban Environment

Risk-Averse Caching Policies for YouTube Content in Femtocell Networks using Density Forecasting

Contact Info

Product

Resources

About