Self-paced learning enhanced neural matrix factorization for noise-aware recommendation

Abstract

Matrix factorization-based collaborative filtering, learning user and item latent features, has been one of the powerful recommendation techniques. Due to its simply modeling of user–item interactions by inner product of two vectors as a linear model, its efficiency needs an improvement. Neural Network-based matrix factorization has been proposed to deal with this issues. Usually, these methods are proposed on clean data, but in real applications, there are possibly unexpected noises and outliers, due to many subjective or objective reasons. The noisy instances would disturb the learning of normal instances and thus cause adverse affect as the model would also be easily over-fitted. Thus, we propose an enhanced neural matrix factorization model by introducing a self-paced learning (SPL) schema, which can automatically distinguish noisy instances and learn the model mostly based on clean instances. The main contribution of our model is that we design a bounded SPL learning schema with a parameter to control how many instances will be finally induced in the model learning. Thus, different from traditional SPL that gradually selects instances until all are selected, the bounded SPL mechanism tries to learn the model mainly on clean data and exclude noisy instances. The effectiveness of proposed method on collaborative filtering is demonstrated by extensive experiments on three widely used datasets.