Comparison of recommendation systems based on deep machine learning methods
Embedding-based models have been used in collaborative filtering over a decade. According to traditional collaborative filtering, the researchers used dot product or similarity measure to combine two or more embeddings. Typically, matrix factorization is the simplest example of an embedding-based model. In recent years, it has been proposed to replace the dot product with deep learning methods, for example, using multi-layer perceptron (MLP) algorithm. This approach is often referred to as neural collaborative filtering (NCF). In this paper, we used NCF in our research, specifically predicting item ratings results and displaying recommendations to users on e-commerce websites. We have applied NCF to the recommender system by using a deep learning model. The article used Olist’s dataset to serve our experiment. We have successfully built a NCF-based recommender system with a large and sparse dataset. We have obtained better results than those produced by other methods.