Please use this identifier to cite or link to this item: https://doi.org/10.1145/3038912.3052569
Title: Neural Collaborative Filtering
Authors: Xiangnan He 
Lizi Liao 
Hanwang Zhang 
Liqiang Nie 
Xia Hu
Tat-Seng Chua 
Keywords: Collaborative filtering
Deep learning
Implicit feedback
Matrix factorization
Neural networks
Issue Date: 4-Mar-2017
Publisher: International World Wide Web Conferences Steering Committee
Citation: Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua (2017-03-04). Neural Collaborative Filtering. WWW 2017 : 173-182. ScholarBank@NUS Repository. https://doi.org/10.1145/3038912.3052569
Rights: Attribution 4.0 International
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. © 2017 International World Wide Web Conference Committee (IW3C2).
Source Title: WWW 2017
URI: https://scholarbank.nus.edu.sg/handle/10635/167392
ISBN: 9781450349130
DOI: 10.1145/3038912.3052569
Rights: Attribution 4.0 International
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