Article

DeepFM: a factorization-machine based neural network for CTR prediction

Authors:

Xiuqiang HeAuthors Info & Claims

IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence

Pages 1725 - 1731

Published: 19 August 2017 Publication History

Abstract

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.

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Cited By

Li HSang LZhang YZhang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SimCEN: Simple Contrast-enhanced Network for CTR PredictionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681203(2311-2320)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681203
Xi YLiu WLin JCai XZhu HZhu JChen BTang RZhang WYu Y(2024)Towards Open-World Recommendation with Knowledge Augmentation from Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688104(12-22)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688104
Bai YZhang YFeng FLu JZang XLei CSong YBaeza-Yates RBonchi F(2024)GradCraft: Elevating Multi-task Recommendations through Holistic Gradient CraftingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671585(4774-4783)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671585
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DeepFM: a factorization-machine based neural network for CTR prediction
1. Computing methodologies
  1. Machine learning
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Information

Published In

cover image Guide Proceedings

IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence

August 2017

5253 pages

ISBN:9780999241103

Editor:
Carles Sierra
IIIA-CSIC

Sponsors

Australian Comp Soc: Australian Computer Society
NSF: National Science Foundation
Griffith University
University of Technology Sydney
AI Journal: AI Journal

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AAAI Press

Publication History

Published: 19 August 2017

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Cited By

Li HSang LZhang YZhang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SimCEN: Simple Contrast-enhanced Network for CTR PredictionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681203(2311-2320)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681203
Xi YLiu WLin JCai XZhu HZhu JChen BTang RZhang WYu Y(2024)Towards Open-World Recommendation with Knowledge Augmentation from Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688104(12-22)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688104
Bai YZhang YFeng FLu JZang XLei CSong YBaeza-Yates RBonchi F(2024)GradCraft: Elevating Multi-task Recommendations through Holistic Gradient CraftingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671585(4774-4783)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671585
Zhang SLiu HBao WYu ESong YBaeza-Yates RBonchi F(2024)A Self-boosted Framework for Calibrated RankingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671570(6226-6235)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671570
Si ZGuan LSun ZZang XLu JHui YCao XYang ZZheng YLeng DZheng KZhang CNiu YSong YGai KSerra ESpezzano F(2024)TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at KuaishouProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680030(4890-4897)Online publication date: 21-Oct-2024
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Han JLi WLi YCai ZSerra ESpezzano F(2024)Quantum Cognition-Inspired EEG-based Recommendation via Graph Neural NetworksProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679564(778-788)Online publication date: 21-Oct-2024
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Sun ZSi ZZang XZheng KSong YZhang XXu JSerra ESpezzano F(2024)Large Language Models Enhanced Collaborative FilteringProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679558(2178-2188)Online publication date: 21-Oct-2024
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Yu YGao CChen JTang HSun YChen QMa WZhang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657868(977-987)Online publication date: 10-Jul-2024
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Chen HLi CChen TChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision MarketingCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648346(461-470)Online publication date: 13-May-2024
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