Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study

The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of vi...

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Autores principales:	Sidney Loyola de Sá, Antonio A. de A. Rocha, Aline Paes
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	popularity prediction video machine learning word embeddings Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8dcec17b11154b34a408a44aed8b95e4
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spelling	oai:doaj.org-article:8dcec17b11154b34a408a44aed8b95e42021-11-11T19:16:26ZPredicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study10.3390/s212173281424-8220https://doaj.org/article/8dcec17b11154b34a408a44aed8b95e42021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7328https://doaj.org/toc/1424-8220The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract the attention of most users. For example, in the data explored in this article, only 6% of the most popular videos account for 85% of total views. Finding out in advance which videos will be popular is not trivial, especially given many influencing variables. Nevertheless, a tool with this ability would be of great value to help dimension network infrastructure and properly recommend new content to users. In this way, this manuscript examines the machine learning-based approaches that have been proposed to solve the prediction of web content popularity. To this end, we first survey the literature and elaborate a taxonomy that classifies models according to predictive features and describes state-of-the-art features and techniques used to solve this task. While analyzing previous works, we saw an opportunity to use textual features for video prediction. Thus, additionally, we propose a case study that combines features acquired through attribute engineering and word embedding to predict the popularity of a video. The first approach is based on predictive attributes defined by resource engineering. The second takes advantage of word embeddings from video descriptions and titles. We experimented with the proposed techniques in a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of engineering features and embeddings using the Random Forest algorithm achieved the best result, with an accuracy of 87%.Sidney Loyola de SáAntonio A. de A. RochaAline PaesMDPI AGarticlepopularity predictionvideomachine learningword embeddingsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7328, p 7328 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	popularity prediction video machine learning word embeddings Chemical technology TP1-1185
spellingShingle	popularity prediction video machine learning word embeddings Chemical technology TP1-1185 Sidney Loyola de Sá Antonio A. de A. Rocha Aline Paes Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study
description	The Internet’s popularization has increased the amount of content produced and consumed on the web. To take advantage of this new market, major content producers such as Netflix and Amazon Prime have emerged, focusing on video streaming services. However, despite the large number and diversity of videos made available by these content providers, few of them attract the attention of most users. For example, in the data explored in this article, only 6% of the most popular videos account for 85% of total views. Finding out in advance which videos will be popular is not trivial, especially given many influencing variables. Nevertheless, a tool with this ability would be of great value to help dimension network infrastructure and properly recommend new content to users. In this way, this manuscript examines the machine learning-based approaches that have been proposed to solve the prediction of web content popularity. To this end, we first survey the literature and elaborate a taxonomy that classifies models according to predictive features and describes state-of-the-art features and techniques used to solve this task. While analyzing previous works, we saw an opportunity to use textual features for video prediction. Thus, additionally, we propose a case study that combines features acquired through attribute engineering and word embedding to predict the popularity of a video. The first approach is based on predictive attributes defined by resource engineering. The second takes advantage of word embeddings from video descriptions and titles. We experimented with the proposed techniques in a set of videos from GloboPlay, the largest provider of video streaming services in Latin America. A combination of engineering features and embeddings using the Random Forest algorithm achieved the best result, with an accuracy of 87%.
format	article
author	Sidney Loyola de Sá Antonio A. de A. Rocha Aline Paes
author_facet	Sidney Loyola de Sá Antonio A. de A. Rocha Aline Paes
author_sort	Sidney Loyola de Sá
title	Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study
title_short	Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study
title_full	Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study
title_fullStr	Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study
title_full_unstemmed	Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study
title_sort	predicting popularity of video streaming services with representation learning: a survey and a real-world case study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8dcec17b11154b34a408a44aed8b95e4
work_keys_str_mv	AT sidneyloyoladesa predictingpopularityofvideostreamingserviceswithrepresentationlearningasurveyandarealworldcasestudy AT antonioadearocha predictingpopularityofvideostreamingserviceswithrepresentationlearningasurveyandarealworldcasestudy AT alinepaes predictingpopularityofvideostreamingserviceswithrepresentationlearningasurveyandarealworldcasestudy
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Predicting Popularity of Video Streaming Services with Representation Learning: A Survey and a Real-World Case Study

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