Predicting the Popularity of Online Videos via Deep Neural Networks

2017/11/29 by Yue Mao, Mao, Yue, Yi Shen +4
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Digital Marketing and Social Media #FOS: Computer and information sciences #Image and Video Quality Assessment #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1711.10718

openalex publication_date 2017/11/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Predicting the popularity of online videos is important for video streaming content providers. This is a challenging problem because of the following two reasons. First, the problem is both "wide" and "deep". That is, it not only depends on a wide range of features, but also be highly non-linear and complex. Second, multiple competitors may be involved. In this paper, we propose a general prediction model using the multi-task learning (MTL) module and the relation network (RN) module, where MTL can reduce over-fitting and RN can model the relations of multiple competitors. Experimental results show that our proposed approach significantly increases the accuracy on predicting the total view counts of TV series with RN and MTL modules.

Citations

• Long Short-Term Memory
• An Overview of Multi-Task Learning in Deep Neural Networks

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