Neural Collaborative Filtering
In this post well cover some of the basics of recommendation system approaches utilising neural networks. Collaborative filtering (CF) is a recommendation generation method that relies on user-item interactions to make predictions about what a user might like based on the historical data of user interaction with the item. We covered collaborative filtration using matrix factorisation is the notebook. Specifically we looked at methods such as SVD in order to generate the user and item matrices, these two matrices are then multiplied together to get the corresponding scores for each user, item pairing; a model-based approach. Neural Collaborative Filtering (NCF) bears some similarity to CF whilst leveraging the benefits of deep learning techniques to enhance recommendation performance.