Neural Collaborative Filtering

TL;DR

This paper introduces NCF, a neural network framework for collaborative filtering in recommender systems, replacing traditional inner product models with deep learning to improve recommendation accuracy on real-world datasets.

Contribution

The paper proposes a novel neural network-based framework for collaborative filtering that generalizes matrix factorization and leverages deep learning for better modeling of user-item interactions.

Findings

NCF outperforms state-of-the-art methods on real-world datasets.

Deeper neural network layers lead to improved recommendation performance.

Replacing inner product with neural networks captures complex user-item interactions.

Abstract

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.