· 2017
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
- 2,258citations
- 2017year
Short summary
DeepFM, a novel neural network architecture, effectively models both low- and high-order feature interactions for CTR prediction without manual feature engineering.
AI-generated from the title and abstract; the full text is not read.
Key points
- DeepFM models both low- and high-order feature interactions for CTR prediction.
- It combines factorization machines and deep learning in a single neural network.
- DeepFM requires no feature engineering beyond raw input features.
- The model shares a common input between its 'wide' and 'deep' components.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source., 2017 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Information Systems
Information SystemsComputer Science