Neural embedding collaborative filtering for recommender systems

Abstract

The main purpose of collaborative filtering algorithm is to provide a personalized recommender system based on past interactions of each user (e.g., clicks and purchases). Among various collaborative filtering techniques, matrix factorization is widely adopted in diverse applications. This technique has superior characteristics, including applying latent feature vectors to represent users or items and projecting users and items into a shared latent feature space. In the present study, a matrix factorization model with the neural embedding called the neural embedding collaborative filtering (NECF) is proposed. In order to evaluate the performance of the proposed method, a probabilistic auto-encoder is initially applied to achieve unsupervised learning to generate the neural embedding vector from the user–item data. Secondly, these vectors are combined with a regression model based on single point negative sampling to represent the latent feature vectors of the user with regression coefficients. Moreover, an inner product is applied on latent features of users and items to determine the correlations between them. It should be indicated that the NECF is generic so that it can express and generalize the matrix factorization under its framework. In the present study, a ridge regression learning is applied on latent features of each user. The experimental results on two benchmark data sets show that the proposed model outperforms other state-of-the-art methods.