PRSAMF: Personalized recommendation based on sentiment analysis and matrix factorization

In the current era of rapid Internet and artificial intelligence development, the explosion of information requires effective filtering to match user interests. Accordingly, this paper proposes a personalized recommendation algorithm based on sentiment analysis and matrix factorization (PRSAMF). A sentiment analysis model is constructed utilizing a Long Short-Term Memory (LSTM) network for deep learning, with ongoing parameter adjustments for training and validation. Through this approach, the LSTM network effectively captures the emotional polarity in user reviews. This emotional polarity, combined with the user rating matrix, enhances the accuracy of representing user reviews. Subsequently, the user sentiment score matrix and the high-frequency matrix of user search items undergo factorization to uncover potential user preferences and item attributes. Recommendations are then made based on the users sentiment towards these attributes. Experimental results on the dataset demonstrate the models effectiveness, showing optimal accuracy and low loss rates in sentiment analysis. Additionally, the error rate remains within acceptable limits, indicating the feasibility and robustness of the proposed recommendation algorithm.