An Engagement Model Based on User Interest and QoS in Video Streaming Systems
With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, esp...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi-Wiley
2018-01-01
|
Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2018/1398958 |
id |
doaj-58fea83bebc14d53a00db8fe6d26e2ad |
---|---|
record_format |
Article |
spelling |
doaj-58fea83bebc14d53a00db8fe6d26e2ad2020-11-24T21:27:43ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772018-01-01201810.1155/2018/13989581398958An Engagement Model Based on User Interest and QoS in Video Streaming SystemsXiaoying Tan0Yuchun Guo1Mehmet A. Orgun2Liyin Xue3Yishuai Chen4Beijing Jiaotong University, ChinaBeijing Jiaotong University, ChinaMacquarie University, AustraliaAustralian Taxation Office, Sydney, NSW 2000, AustraliaBeijing Jiaotong University, ChinaWith the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.http://dx.doi.org/10.1155/2018/1398958 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaoying Tan Yuchun Guo Mehmet A. Orgun Liyin Xue Yishuai Chen |
spellingShingle |
Xiaoying Tan Yuchun Guo Mehmet A. Orgun Liyin Xue Yishuai Chen An Engagement Model Based on User Interest and QoS in Video Streaming Systems Wireless Communications and Mobile Computing |
author_facet |
Xiaoying Tan Yuchun Guo Mehmet A. Orgun Liyin Xue Yishuai Chen |
author_sort |
Xiaoying Tan |
title |
An Engagement Model Based on User Interest and QoS in Video Streaming Systems |
title_short |
An Engagement Model Based on User Interest and QoS in Video Streaming Systems |
title_full |
An Engagement Model Based on User Interest and QoS in Video Streaming Systems |
title_fullStr |
An Engagement Model Based on User Interest and QoS in Video Streaming Systems |
title_full_unstemmed |
An Engagement Model Based on User Interest and QoS in Video Streaming Systems |
title_sort |
engagement model based on user interest and qos in video streaming systems |
publisher |
Hindawi-Wiley |
series |
Wireless Communications and Mobile Computing |
issn |
1530-8669 1530-8677 |
publishDate |
2018-01-01 |
description |
With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context. |
url |
http://dx.doi.org/10.1155/2018/1398958 |
work_keys_str_mv |
AT xiaoyingtan anengagementmodelbasedonuserinterestandqosinvideostreamingsystems AT yuchunguo anengagementmodelbasedonuserinterestandqosinvideostreamingsystems AT mehmetaorgun anengagementmodelbasedonuserinterestandqosinvideostreamingsystems AT liyinxue anengagementmodelbasedonuserinterestandqosinvideostreamingsystems AT yishuaichen anengagementmodelbasedonuserinterestandqosinvideostreamingsystems AT xiaoyingtan engagementmodelbasedonuserinterestandqosinvideostreamingsystems AT yuchunguo engagementmodelbasedonuserinterestandqosinvideostreamingsystems AT mehmetaorgun engagementmodelbasedonuserinterestandqosinvideostreamingsystems AT liyinxue engagementmodelbasedonuserinterestandqosinvideostreamingsystems AT yishuaichen engagementmodelbasedonuserinterestandqosinvideostreamingsystems |
_version_ |
1725973760290324480 |