Predicting video engagement using heterogeneous DeepWalk

Chaturvedi, Iti, Thapa, Kishor, Cavallari, Sandro, Cambria, Erik, and Welsch, Roy E. (2021) Predicting video engagement using heterogeneous DeepWalk. Neurocomputing, 465. pp. 228-237.

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Abstract

Video engagement is important in online advertisements where there is no physical interaction with the consumer. Engagement can be directly measured as the number of seconds after which a consumer skips an advertisement. In this paper, we propose a model to predict video engagement of an advertisement using only a few samples. This allows for early identification of poor quality videos. This can also help identify advertisement frauds where a robot runs fake videos behind the name of well-known brands. We leverage on the fact that videos with high engagement have similar viewing patterns over time. Hence, we can create a similarity network of videos and use a graph-embedding model called DeepWalk to cluster videos into significant communities. The learned embedding is able to identify viewing patterns of fraud and popular videos. In order to assess the impact of a video, we also consider how the view counts increase or decrease over time. This results in a heterogeneous graph where an edge indicates similar video engagement or history of view counts between two videos. Since it is difficult to find labelled samples for ‘fraud’ video, we leverage on a one-class model that can determine ‘fraud’ videos with outlier or abnormal behavior. The proposed model outperforms baselines in F-measure by over 20%.

Item ID: 69402
Item Type: Article (Research - C1)
ISSN: 1872-8286
Keywords: Video engagement; Deep Walk; Online behavior; One-class model
Copyright Information: © 2021 Elsevier B.V. All rights reserved.
Date Deposited: 21 Sep 2021 02:05
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460206 Knowledge representation and reasoning @ 40%
47 LANGUAGE, COMMUNICATION AND CULTURE > 4702 Cultural studies > 470208 Culture, representation and identity @ 20%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation @ 40%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 80%
13 CULTURE AND SOCIETY > 1399 Other culture and society > 139999 Other culture and society not elsewhere classified @ 20%
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