mlsanityData to 14 Oct 2026

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

arXiv 1703.04247 ↗First posted 13 Mar 2017cs.IR cs.CLcited by 676

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Abstract (arXiv)

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.

Key facts. Cited by 676 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2017.

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Citing papers per year: 2020: 1; 2021: 109; 2022: 118; 2023: 152; 2024: 107; 2025: 110; 2026: 79.

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