Public Interest Analysis Based on Implicit Feedback of IPTV Users

Kren, Matej; Kos, Andrej; Zhang, Yuwei; Kos, Anton; Sedlar, Urban

doi:10.1109/tii.2017.2695371

Cited by 7 publications

(12 citation statements)

References 31 publications

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“…However, success prediction cannot be made before the movie is released. In 2015, Lash and Zhao proposed a way to predict decisions about movie investments [6]. This work provided help with investment decision making early in movie production.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The discovery of such association rules has been discussed in the literature. In this context, sequential association rules [5], time intervals for association rules [6], [7] and calendarbased association rules [4], [8] are some interesting studies in recent years. For this work, data mining process was used to extract patterns and trends which can be beneficial in predicting movies success.…”

Section: Introductionmentioning

confidence: 99%

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Flick Fortune: Movie Gross Prediction using Data Analysis and Prediction

Balamurugan¹

2018

IJRASET

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Data Mining is one of the rapid growing areas in the development of Industrial automation. Many researches are carried out in order to determine the future result of any business such as stock market. The appropriate data mining technique helps the decision makers to arrive upon a valid decision. Our proposed system is the implementation of probabilistic prediction of data from the extensive analysis. We coherently apply the proposed system onto the movie prediction such as Gross collection, Rating, feedback and success ratio. We use web scrapper code with TMDB API to fetch attributes of the movies such as actor name, director, musician, producer and budget. We employ association rule mining to extract the probabilistic prediction from the user query. We further use AJAX for efficient and optimistic data traffic between the client and the server. Experimental results show that our approach is best performing the data analysis under several test strategies

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flick Fortune: Movie Gross Prediction using Data Analysis and Prediction

Balamurugan¹

2018

IJRASET

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“…Analysis of a pseudonymized sample of user-generated diagnostic events in a large-scale IPTV system has inspired our previous [5], [6] and current work. Such systems are a good source of implicit feedback data, and have the potential for expansion into large-scale data collection, without disrupting the quality of experience.…”

Section: Introductionmentioning

confidence: 99%

“…The motivation of this work is to continue our previous research [6], where we showed correlation between implicit user feedback and public opinion, but lack of explicit test data prevented us from building an accurate model. In this work we thus focus on building and validating such a model.…”

Section: Introductionmentioning

confidence: 99%

“…In this work we thus focus on building and validating such a model. The starting points of the research remain similar as in [6]: we focus primarily on channel change events (CCE) generated by the viewers. This data basically conveys user behavior information, as each channel change event is motivated by a combination of viewer's interests and content context [8].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Opinion of IPTV Viewers Based on Implicit Feedback and Content Metadata

2019

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In today's hyper-connected world, rich social data feeds can be obtained from various sources, including the data exhaust of many commonly used systems. In this paper, we analyze the social pulse, obtained from viewer activity in an IPTV network-we attempt to validate a framework for determining public opinion and public interest through implicit feedback of IPTV viewers. First, we address the hypothesis that implicit viewer feedback in the form of channel change events, paired with the content metadata, can be used to model viewers' opinion and interest. For this, we design a controlled experiment to collect explicit feedback by rating a set of general-interest news clips. In addition to collecting demographic information, we also survey viewers' opinion, interest, and the probability of channel change during each clip. Furthermore, we extract weighted feature vectors from the closed captions of the video; this data, combined with the reported probability of channel change, is used to build a model that classifies opinion in five categories based on the probability of channel change and content. Next, we build a simplified model that classifies opinion in five categories based on the interest, which shows a linear relationship, but further consideration of content, in this case, provides better accuracy and possibility to analyze anomalous cases. Finally, we discuss and analyze the applications of such models in large systems and the necessary modifications to scale the system and to ensure the adequate performance on massive IPTV event data streams. INDEX TERMS Implicit user feedback, opinion modeling, interest modeling, viewership profiling, IPTV.

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Effect of VR technology matureness on VR sickness

Geršak

Guna

2018

Multimed Tools Appl

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Public Interest Analysis Based on Implicit Feedback of IPTV Users

Cited by 7 publications

References 31 publications

Flick Fortune: Movie Gross Prediction using Data Analysis and Prediction

Flick Fortune: Movie Gross Prediction using Data Analysis and Prediction

Modeling Opinion of IPTV Viewers Based on Implicit Feedback and Content Metadata

Effect of VR technology matureness on VR sickness

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