Quantitative causality analysis of viewing abandonment reasons using Shapley value
Sosa Akimoto, Pierre Lebreton, Shoko Takahashi, Kazuhisa Yamagishi · 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) · 2022
As adaptive bitrate streaming services are widely used, video streaming providers need to know what factors impact viewing abandonments. As quality and content-related factors are known to influence viewing abandonments, the reasons for viewing abandonments need to be analyzed while considering both factors. For this purpose, previous studies developed machine learning models, but most of them lack the interpretability of the relationships between explanatory variables and a target variable. In addition, causal relationships among explanatory variables need to be considered to disambiguate the effect of each variable. In this paper, we propose using Asymmetric Shapley value (ASV) to study the interpretability of a developed machine learning model and to take into account the effect of causalities among explanatory variables. We used a dataset collected in laboratory experiments on adaptive bitrate streaming and built binary classification models that classify viewing abandonment reasons into quality-induced or content-induced. We selected random forests as the best model and analyzed the relationships between explanatory variables (application quality, users' behavior, content attribute) and the target variable (viewing abandonment reason) with ASV. The results provided meaningful insights into the relationships and showed assuming causality is helpful to modify the estimation of the relationships