Vector space model embedding for recomender system neural networks

Research shows that recommendation algorithms such as Collaborative Filtering (CF) can be enhanced using neural network (NN) to make more accurate recommendations. This project proposes an adaptive recommendation model based on NN to quickly access different items. A vector space embedding method is used to vectorize users and items before a Deep Neural Network (DNN) rating prediction network model is used to predict users' rating behavior. Multi-domain datasets are utilized in experiments to evaluate results and compared our method with traditional recommendation algorithms. Results show that the improved NN based recommendation model is more effective and achieves a higher score in both similarity calculation and predication.