Exploring LSTM-CAMF: A New Approach for Context-Aware Collaborative Filtering
To produce more accurate recommendations, Context-Aware Recommender Systems (CARS) incorporate contextual elements during user interactions. However, a major challenge lies in the need for additional contextual data, which can hinder the performance of collaborative filtering techniques. In this research, we introduce an innovative approach for detecting contextual information in real time by integrating Long Short-Term Memory (LSTM) recurrent neural networks with Context-Aware Matrix Factorization (CAMF). This strategy is designed to dynamically adjust to changes