To adapt the proposed framework to account for content-specific factors that influence user quality perception, several strategies can be implemented:
Feature Engineering for Content Attributes: The framework can be enhanced by incorporating additional features that represent content-specific attributes, such as video genre, resolution, and codec. For instance, different genres may have varying user expectations regarding quality, which can be captured through categorical variables in the dataset.
Segmentation of Data: By segmenting the dataset based on content characteristics, the framework can train separate models for different types of content. This approach allows for more tailored QoE predictions that consider the unique quality expectations associated with each content type.
User Profiling: Implementing user profiling techniques can help identify individual user preferences related to content. By analyzing historical data on user interactions and feedback, the framework can personalize QoE predictions based on the specific content characteristics that resonate with each user.
Incorporating Content Analysis: Utilizing machine learning techniques to analyze the content itself (e.g., visual complexity, motion, and audio characteristics) can provide insights into how these factors impact user perception. This analysis can be integrated into the QoE prediction model to enhance its accuracy.
Dynamic Adjustment of QoE Metrics: The framework can be designed to dynamically adjust QoE metrics based on content-specific factors. For example, the acceptable levels of buffering or resolution may vary depending on the content type, and the model can be trained to reflect these variations.
By implementing these adaptations, the framework can provide a more nuanced understanding of how content-specific factors influence user quality perception, leading to more accurate QoE predictions and improved user satisfaction across diverse multimedia services.