To enhance the P4R framework by incorporating sequential information and user-specific preferences, several strategies can be employed. First, integrating a sequential recommendation model, such as a Recurrent Neural Network (RNN) or a Transformer-based architecture, can help capture the temporal dynamics of user interactions. This would allow the model to consider the order of user-item interactions, thereby improving the understanding of user preferences over time.
Additionally, user-specific preferences can be integrated through personalized embeddings that reflect individual user behavior patterns. This can be achieved by utilizing user history data to create dynamic user profiles that evolve based on recent interactions. By combining these personalized embeddings with the existing item profiles generated through the prompting strategy, the P4R framework can provide more tailored recommendations.
Moreover, implementing attention mechanisms can help the model focus on relevant past interactions when generating recommendations, allowing it to weigh the importance of different interactions based on their recency and relevance. This approach would not only enhance the accuracy of the recommendations but also ensure that they are more aligned with the user's current interests and preferences.