.png){kind=icon}
返回
A Novel Method for IPTV Customer Behavior Analysis Using Time Series
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2022.3164409.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Internet Protocol Television (IPTV) has had a significant impact on live TV content consumption in the past decade, as improvements in the broadband speed have allowed more data volume to be delivered. In addition to existing infrastructure, which is mostly based on the set top boxes, new content providers have emerged, utilizing newly developed proprietary streaming platforms. As the number of IPTV users grew, more volume and variety of data became available for analysis. By analyzing stored user actions, it is possible to create a multivariate time series that represents user behavior over time. The approach presented in the paper is based on multivariate time series generation from user data and determining the similarity between them. Time series are created for each user based on the proposed quantified action sets, grouped in the feature groups and summarized over time. The action sets and feature groups can be adjusted to a certain IPTV platform. The end result of the analysis is the similarity score matrix, generated by calculating the similarities of all users' time series, where the similarity measure calculation can be chosen arbitrarily.
Keyword:
Time series analysis
IPTV
Recommender systems
TV
Licenses
Fingerprint recognition
Classification algorithms
IPTV
time series analysis
data analysis
user behavior analysis
time series similarity
user profiling
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Smooth Estimation of a Lifetime Distribution with Competing Risks by Using Regular Interval Observations: Application to Cocoa Fruits Growth通过使用规则间隔观测平滑估计竞争风险下的寿命分布：在可可果实生长中的应用
OAAI
OAAI分享收藏分享收藏User Behavior Fingerprinting With Multi-Item-Sets and Its Application in IPTV Viewer Identification多项目集用户行为指纹及其在IPTV观众识别中的应用
PREAI
分享收藏分享收藏
查看更多内容