To enhance the accuracy of Quality of Experience (QoE) predictions within the proposed machine learning-driven framework, integrating user feedback and subjective quality assessments is essential. This can be achieved through several strategies:
User Surveys and Feedback Mechanisms: Implementing real-time surveys or feedback forms within the multimedia service can allow users to provide subjective ratings of their experience immediately after content consumption. This data can be collected alongside objective metrics, such as delay and jitter, to create a more comprehensive dataset.
Crowdsourced Data Collection: By leveraging crowdsourcing platforms, the framework can gather subjective assessments from a diverse user base across different demographics and geographical locations. This approach can help capture a wide range of user experiences and preferences, enriching the dataset used for training the machine learning models.
Sentiment Analysis: Utilizing natural language processing (NLP) techniques to analyze user comments and reviews on social media or within the application can provide insights into user satisfaction and dissatisfaction. This qualitative data can be transformed into quantitative metrics that can be integrated into the QoE prediction model.
Adaptive Learning: The framework can be designed to adaptively learn from user feedback over time. By continuously updating the machine learning model with new data, including user ratings and comments, the framework can refine its predictions and better align with user expectations.
Hybrid Models: Combining objective network parameters with subjective user feedback can lead to the development of hybrid models that leverage both types of data. This approach can improve the robustness of QoE predictions by accounting for the subjective nature of user experiences.
By incorporating these strategies, the framework can achieve a more holistic understanding of QoE, leading to improved accuracy in predicting user satisfaction and enabling more effective resource allocation in multimedia services.