Time Context-Aware IPTV Program Recommendation Based on Tensor Learning

Yin, Xiaoyan; Chen, Yanjiao; Mi, Xiaoqian; Wang, Hua; Tang, Zhangyong; Fang, Dingyi

doi:10.1109/glocom.2018.8647211

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…A different approach has to be taken for digital terrestrial television models [4] compared to the video-on-demand models [5]. Several other approaches for IPTV recommendation model have been proposed, such as using transformed-based fusion [6] and time-context aware model based on Tensor learning [7]. Consumer feedback can also be explicit [5], which relies on the user providing explicit rating to the system.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for IPTV Customer Behavior Analysis Using Time Series

Hlupic¹,

Orescanin²,

Baranović

2022

IEEE Access

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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.INDEX TERMS IPTV, time series analysis, data analysis, user behavior analysis, time series similarity, user profiling.

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Section: Related Workmentioning

confidence: 99%

A Novel Method for IPTV Customer Behavior Analysis Using Time Series

Hlupic¹,

Orescanin²,

Baranović

2022

IEEE Access

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“…The papers by Yang et al [15], Yin et al [16], and Gao et al [17] can be given as recent examples on recommendation systems dedicated to the IPTV domain. For a history of the evolution of recommendation engines, the work by Smyth and Cotter can be cited [18].…”

Section: Literature Surveymentioning

confidence: 99%

User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks

Topalli¹,

Kılınç²

2020

Turk J Elec Eng & Comp Sci

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In this paper, an earlier method proposed by the authors to make smart recommendations utilizing artificial intelligence and the latest technologies developed for the television area is expanded further using controlled clustering with genetic algorithms (CCGA). For this purpose, genetic algorithms (GAs), artificial neural networks (ANNs), and hybrid broadcast broadband television (HbbTV) are combined to get the users' television viewing habits and to create profiles. Then television programs are recommended to the users based on that profiling. The data gathered by the developed HbbTV application for previous studies are reused in this study. These data are employed to cluster users. The number of clusters is found by CCGA, a method proposed in this paper. For each cluster formed by CCGA, a separate ANN is designed to learn the viewing habits of the users of the corresponding cluster. The weight matrices are initialized also by GA. The recommendations produced using the proposed model are then presented by the same HbbTV application developed by the authors. Clustering with GAs gives better results when compared to the well-known K-means clustering algorithm.

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“…Still, it can instead identify compelling titles among less popular items that would otherwise be hard to find [11] [12]. Furthermore, to evaluate the recommendation model, it is essential to know how many items are suggested [13].…”

Section: Introductionmentioning

confidence: 99%

A Recommendation System Based on Implicit Data for Internet Protocol Television (IPTV)

Mansour,

Omran,

Kaddoura

et al. 2023

AJSE

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IPTV delivers television content over Internet Protocol (IP) networks. Videos On Demand (VOD) is the most popular IPTV, allowing users to freely select from a vast pool of program genres. Therefore, it is necessary to introduce innovative features to attract new users and retain existing ones. For this purpose, IPTV systems typically use VOD recommendation engines. The primary purpose of recommendation systems is to suggest user-relevant items from various items by producing a list of recommendations for each user. In this paper, we introduce an approach to recommendation systems in IPTV. We developed this approach on implicit feedback derived from users’ interaction with movies/series sets, such as how many times they watched a movie and how long they have spent watching specific movies/series. For the previous factors, we tested a variety of recommendation algorithms, content-based, collaborative-based, and hybrid. Then applied the previously mentioned algorithms on real-life big data sets after introducing some modifications to the algorithms, then benchmarked the results on multiple performance metrics. We noticed that the applied changes achieved promising results.

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Time Context-Aware IPTV Program Recommendation Based on Tensor Learning

Cited by 3 publications

References 15 publications

A Novel Method for IPTV Customer Behavior Analysis Using Time Series

A Novel Method for IPTV Customer Behavior Analysis Using Time Series

User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks

A Recommendation System Based on Implicit Data for Internet Protocol Television (IPTV)

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