Research on user book rating prediction based on deep learning

I developed a deep learning-based recommendation system to enhance book recommendation accuracy using three datasets: Books, Ratings, and Users. After data cleaning and preprocessing, including handling missing values and standardizing formats, I used TF-IDF vectorization and cosine similarity for content-based recommendations. Using TensorFlow and Keras, I built a collaborative filtering model with embedding layers, regularization, and dropout to prevent overfitting. The model was trained with the Adam optimizer and evaluated using RMSE. Training involved adjusting hyperparameters to find optimal values. The deep learning model was compared with traditional methods like Singular Value Decomposition (SVD), Item-based Collaborative Filtering, and Alternating Least Squares (ALS), and showed significant improvement in recommendation accuracy. The model effectively handles large-scale data and enhances recommendation quality. In conclusion, my deep learning-based system, along with the SVD, Item-based Collaborative Filtering, and ALS models, significantly improves book recommendation accuracy, offering valuable insights for future research and development in recommendation systems. Finally, I selected the neural network model and further fine-tuned its parameters to achieve the best performance.