Keywords-recommendation system; machine learning; Non-groups based on their viewing patterns. Agent Recurrent Deterministic Policy Gradient (MA-RDPG) The proliferation of digital content has necessitated the algorithm, as suggested by Zhao et al., this research aims to development of effective recommendation systems to aid users optimize overall system performance through enhanced in navigating vast amounts of data. This research aims to explore and implement advanced machine Previous studies have extensively explored collaborative learning techniques [1-6] to create a high-performing movie filtering techniques for recommendation systems. The study addresses the following (2001) [13] demonstrated the effectiveness of matrix research questions: What are the most effective machine factorization in uncovering latent user-item interactions. How do et al. (2009) [14] further refined these techniques, leading to these models compare in terms of accuracy and relevance?

A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the training process, the model's loss value decreases from 0.65 to 0.31, which shows that the model gradually reduces the discrepancy between the prediction results and the actual labels, and improves the accuracy and generalisation ability.The final loss value of 0.31 indicates that the model fits the training data well, and is able to make predictions and classifications more accurately. The confusion matrix for the training set shows a total of 177 correct predictions and 52 incorrect predictions, with an accuracy of 77%, precision of 88%, recall of 77% and f1 score of 82%. The confusion matrix for the test set shows a total of 167 correct and 53 incorrect predictions with 75% accuracy, 87% precision, 57% recall and 69% f1 score. In summary, the classification prediction algorithm based on LSTM with improved AdaBoost shows good prediction ability for virtual reality user experience. This study is of great significance to enhance the application of virtual reality technology in user experience. By combining LSTM and AdaBoost algorithms, significant progress has been made in user experience prediction, which not only improves the accuracy and generalisation ability of the model, but also provides useful insights for related research in the field of virtual reality. This approach can help developers better understand user requirements, optimise virtual reality product design, and enhance user satisfaction, promoting the wide application of virtual reality technology in various fields.