Neural Collaborative Filtering

by   Xiangnan He, et al.

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.


page 1

page 2

page 3

page 4


Collaborative Item Embedding Model for Implicit Feedback Data

Collaborative filtering is the most popular approach for recommender sys...

A collaborative filtering model with heterogeneous neural networks for recommender systems

In recent years, deep neural network is introduced in recommender system...

Cross-Attribute Matrix Factorization Model with Shared User Embedding

Over the past few years, deep learning has firmly established its prowes...

Neural Cross-Domain Collaborative Filtering with Shared Entities

Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate ...

Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

Recently, recommender systems play a pivotal role in alleviating the pro...

Searching for Interaction Functions in Collaborative Filtering

Interaction function (IFC), which captures interactions among items and ...

Simultaneous Learning of the Inputs and Parameters in Neural Collaborative Filtering

Neural network-based collaborative filtering systems focus on designing ...

Please sign up or login with your details

Forgot password? Click here to reset