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Article overview
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Recurrent Neural Networks for Online Video Popularity Prediction | | Tomasz Trzcinski
; Pawel Andruszkiewicz
; Tomasz Bochenski
; Przemyslaw Rokita
; | | Date: |
21 Jul 2017 | | Abstract: | In this paper, we address the problem of popularity prediction of online
videos shared in social media. We prove that this challenging task can be
approached using recently proposed deep neural network architectures. We cast
the popularity prediction problem as a classification task and we aim to solve
it using only visual cues extracted from videos. To that end, we propose a new
method based on a Long-term Recurrent Convolutional Network (LRCN) that
incorporates the sequentiality of the information in the model. Results
obtained on a dataset of over 37’000 videos published on Facebook show that
using our method leads to over 30% improvement in prediction performance over
the traditional shallow approaches and can provide valuable insights for
content creators. | | Source: | arXiv, 1707.6807 | | Services: | Forum | Review | PDF | Favorites | |
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