A Novel Method for IPTV Customer Behavior Analysis Using Time Series
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 pr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9748119/ |
| _version_ | 1811342181615534080 |
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| author | Tomislav Hlupic Drazen Orescanin Mirta Baranovic |
| author_facet | Tomislav Hlupic Drazen Orescanin Mirta Baranovic |
| author_sort | Tomislav Hlupic |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T19:06:58Z |
| format | Article |
| id | doaj.art-710e4ea652ad4fcb9cc7c6adb49334b9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T19:06:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-710e4ea652ad4fcb9cc7c6adb49334b92022-12-22T02:33:57ZengIEEEIEEE Access2169-35362022-01-0110370033701510.1109/ACCESS.2022.31644099748119A Novel Method for IPTV Customer Behavior Analysis Using Time SeriesTomislav Hlupic0https://orcid.org/0000-0003-0814-8774Drazen Orescanin1https://orcid.org/0000-0002-0233-3971Mirta Baranovic2https://orcid.org/0000-0002-8941-5485Poslovna Inteligencija d. o. o, Zagreb, CroatiaPoslovna Inteligencija d. o. o, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaInternet 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.https://ieeexplore.ieee.org/document/9748119/IPTVtime series analysisdata analysisuser behavior analysistime series similarityuser profiling |
| spellingShingle | Tomislav Hlupic Drazen Orescanin Mirta Baranovic A Novel Method for IPTV Customer Behavior Analysis Using Time Series IEEE Access IPTV time series analysis data analysis user behavior analysis time series similarity user profiling |
| title | A Novel Method for IPTV Customer Behavior Analysis Using Time Series |
| title_full | A Novel Method for IPTV Customer Behavior Analysis Using Time Series |
| title_fullStr | A Novel Method for IPTV Customer Behavior Analysis Using Time Series |
| title_full_unstemmed | A Novel Method for IPTV Customer Behavior Analysis Using Time Series |
| title_short | A Novel Method for IPTV Customer Behavior Analysis Using Time Series |
| title_sort | novel method for iptv customer behavior analysis using time series |
| topic | IPTV time series analysis data analysis user behavior analysis time series similarity user profiling |
| url | https://ieeexplore.ieee.org/document/9748119/ |
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