## 3 Experimental Results and Analysis
### Influence of Different Parameter Combinations on Model Accuracy  
To directly address the role of each influencing factor such as video content, video quality, fluency and volatility on the accuracy of the model, this paper combines different parameters and establishes the QoE models, separately, by the ridge regression method. The specific parameter combination and the corresponding five QoE models established are listed in Tables 3 and 4. The comparison results of different models are listed. As can be seen from Tables 3 and 4, the proposed QoE model (Model 1) can obtain the highest PLCC and SROCC values. For the Waterloo Streaming QoE Database-III dataset, they can reach 0.9570 and 0.9465, respectively, and for the LIVE-NFLX-II dataset, 0.9756 and 0.9718, which can accurately evaluate and predict the user's QoE. From the comparison results, the conclusions can been drawn that: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**QoE Models**} \\ \cline{2-5}  & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** \\ \hline Video content & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) \\ Video quality & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) \\ Fluency & \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) \\ Volatility & \(\surd\) & \(\surd\) & \(\surd\) & & \\ Feature & 62 & 12 & 55 & 59 & 60 \\
**PLCC** & **0.9756** & **0.7083** & **0.4749** & **0.9384** & **0.9384** \\
**SROCC** & **0.9718** & **0.7054** & **0.8496** & **0.9112** & **0.9265** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Influence of different parameters on model accuracy (LIVE-NFLX-II). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Waterloo Streaming QoE Database-III**} & \multicolumn{2}{c}{**LIVE-NFLX-II**} \\ \cline{2-5}  & **PLCC** & **SROCC** & **PLCC** & **SROCC** \\ \hline Bayesian regression & 0.9274 & 0.9054 & 0.9543 & 0.9507 \\ LASSO & 0.8936 & 0.8737 & 0.7969 & 0.8057 \\ SVR & 0.6394 & 0.5981 & 0.6830 & 0.6668 \\ Decision tree & 0.8615 & 0.7858 & 0.8722 & 0.8581 \\ Random forest & 0.8268 & 0.8381 & 0.7659 & 0.8204 \\ Xgboost method & 0.9324 & 0.9133 & 0.8590 & 0.8465 \\ LightGBM regression & 0.8574 & 0.8462 & 0.8402 & 0.8462 \\ ElasticNet regression & 0.9357 & 0.9327 & 0.9484 & 0.9465 \\ Ridge regression & **0.9570** & **0.9465** & **0.9756** & **0.9718** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of models established by multiple regression methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{5}{c}{**QoE Models**} \\ \cline{2-5}  & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 5** \\ \hline Video content & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) \\ characteristics & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) \\ Video quality & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) \\ Fluency & \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) \\ Volatility & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \\ Feature & 68 & 18 & 56 & 64 & 66 \\ dimension & **PLCC** & **0.9570** & **0.8664** & **0.3085** & **0.8002** & **0.8258** \\
**SROCC** & **0.9465** & **0.8243** & **0.3613** & **0.7265** & **0.7721** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of different parameters on model accuracy (Waterloo Streaming QoE Database-III). 1. The parameters of video content characteristics have an extremely important impact on the prediction accuracy of QoE models. Compared with Model 2, Model 1 takes the video content characteristics into account, and PLCC and SROCC increase by 9.06% and 12.22% on Waterloo Streaming QoE Database-III and by 26.73% and 26.64% on LIVE-NFLX-II dataset. 2. Video quality fluctuation is another essential factor affecting QoE.