Papers › DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

13 Mar 2017arXiv:1703.04247archive 2025-07-28

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He

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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Code

Syntology Ran 2 of 8 code samples harvested from 2 repositories linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · fixture could not drive it.

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23 repositories listed; official and paper-mentioned ones first.

xue-pai/FuxiCTR officialmentioned on GitHubpytorch report
UlionTse/mlgb mentioned on GitHubpytorch report
baiyimeng/umc mentioned on GitHubpytorchMIT report
codlife/NLP mentioned on GitHub report
hzn666/rlbid_ea mentioned on GitHubpytorch report
massquantity/LibRecommender mentioned on GitHubtfMIT report
objectc/DeepFM mentioned on GitHubtfMIT report
saulhazelius/deepFM mentioned on GitHubpytorch report
shenweichen/DeepCTR mentioned on GitHubtf report
shiwang0211/kdd_cup_2019 mentioned on GitHubtf report
shenweichen/DeepCTR-Torch pytorchApache-2.0 report

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8 samples harvested; 2 ran; 1 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
1ran · fixture could not drive it
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predict_test_file Leavingseason/OpenLearning4DeepRecsys/models/deepFM.py community (archive-listed) ran · honoured contract no licence file found · pointer only · 78f755dc5ae33d82 · report
prepare_data_4_sp Leavingseason/OpenLearning4DeepRecsys/models/deepFM.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · d48717330763a7f7 · report
build_input_features baiyimeng/umc/models/inputs.py community (archive-listed) unverified MIT (permissive) · 2fb349ac030ba393 · report
computeIntegrand baiyimeng/umc/calib/ParallelNeuralIntegral.py community (archive-listed) unverified MIT (permissive) · 549ad91de4b5b9d4 · report
compute_cc_weights baiyimeng/umc/calib/ParallelNeuralIntegral.py community (archive-listed) unverified MIT (permissive) · dc4044ceb6fafbbd · report
get_data baiyimeng/umc/pretrain.py community (archive-listed) unverified MIT (permissive) · afd61cb63336e329 · report
get_feature_names baiyimeng/umc/models/inputs.py community (archive-listed) unverified MIT (permissive) · ba1c9d87dd13207a · report
integrate baiyimeng/umc/calib/ParallelNeuralIntegral.py community (archive-listed) unverified MIT (permissive) · 152b5034b4ef794e · report

Tasks

Click-Through Rate PredictionFeature EngineeringRecommendation Systems

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Click-Through Rate Prediction Amazon DeepFM AUC 0.8683 #3 of 5 Archive leaderboard report
Click-Through Rate Prediction Bing News DeepFM AUC 0.8376 #3 of 7 Archive leaderboard report
Click-Through Rate Prediction Bing News DeepFM Log Loss 0.2671 #3 of 7 Archive leaderboard report
Click-Through Rate Prediction Company* DeepFM AUC 0.8715 #1 of 8 Archive leaderboard report
Click-Through Rate Prediction Company* DeepFM Log Loss 0.02618 #1 of 8 Archive leaderboard report
Click-Through Rate Prediction Criteo DeepFM AUC 0.8007 #33 of 39 Archive leaderboard report
Click-Through Rate Prediction Criteo DeepFM Log Loss 0.45083 #33 of 39 Archive leaderboard report
Click-Through Rate Prediction Dianping DeepFM AUC 0.8481 #2 of 5 Archive leaderboard report
Click-Through Rate Prediction Dianping DeepFM Log Loss 0.3333 #2 of 5 Archive leaderboard report
Click-Through Rate Prediction KKBox DeepFM AUC 0.8531 #6 of 6 Archive leaderboard report
Click-Through Rate Prediction MovieLens 20M DeepFM AUC 0.7324 #4 of 6 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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