research-article

Neural Collaborative Filtering

Authors:

Tat-Seng ChuaAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 173 - 182

https://doi.org/10.1145/3038912.3052569

Published: 03 April 2017 Publication History

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.

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Index Terms

Neural Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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NExT research is supported by the National Research Foundation

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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https://doi.org/10.26599/TST.2023.9010148
He ZLin JWang CGuizani M(2025)Interaction-knowledge semantic alignment for recommendationNeural Networks10.1016/j.neunet.2024.106755181(106755)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106755
Meydani EDuesing CTrier M(2025)Modeling higher-order social influence using multi-head graph attention autoencoderInformation Systems10.1016/j.is.2024.102474128(102474)Online publication date: Feb-2025
https://doi.org/10.1016/j.is.2024.102474
Jia HHou PZhou YZhu HChai H(2025)LacGCL: Lightweight message masking with linear attention and cross-view interaction graph contrastive learning for recommendationInformation Processing & Management10.1016/j.ipm.2024.10393062:1(103930)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103930
Zeng JWang NLi J(2025)Knowledge-driven hierarchical intents modeling for recommendationExpert Systems with Applications10.1016/j.eswa.2024.125361259(125361)Online publication date: Jan-2025
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Sang LZhang YZhang YLi HZhang Y(2025)Towards similar alignment and unique uniformity in collaborative filteringExpert Systems with Applications10.1016/j.eswa.2024.125346259(125346)Online publication date: Jan-2025
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Heshmati AMeghdadi MAfsharchi MAhmadian S(2025)SiSRS: Signed social recommender system using deep neural network representation learningExpert Systems with Applications10.1016/j.eswa.2024.125205259(125205)Online publication date: Jan-2025
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Zhang XGuo J(2024)A feature-enhanced knowledge graph neural network for machine learning method recommendationPeerJ Computer Science10.7717/peerj-cs.228410(e2284)Online publication date: 28-Aug-2024
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Junxi YWang ZChen C(2024)GCN-MF: A graph convolutional network based on matrix factorization for recommendationInnovation & Technology Advances10.61187/ita.v2i1.302:1(14-26)Online publication date: 8-Apr-2024
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Hwang IKim HKim YLee Y(2024)Generalized neural collaborative filteringKorean Journal of Applied Statistics10.5351/KJAS.2024.37.3.31137:3(311-322)Online publication date: 30-Jun-2024
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