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DeepFM: shared embeddings for low- and high-order feature learning

Jointly trained a factorization-machine branch and deep neural branch sharing raw-feature embeddings, combining low-order interactions and learned nonlinear features.

What is recorded here?

Jointly trained a factorization-machine branch and deep neural branch sharing raw-feature embeddings, combining low-order interactions and learned nonlinear features.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

Inherits FM, MLP and Wide&Deep principles; not invention of them. Reduced manual cross-feature design is not elimination of all preprocessing or a guarantee that arbitrary high-order interactions are exactly learned. CTR benchmark gains do not establish causal effects or long-term user welfare.

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People and systems

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

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

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English catalogue narrative

DeepFM: shared embeddings for low- and high-order feature learning

Jointly trained a factorization-machine branch and deep neural branch sharing raw-feature embeddings, combining low-order interactions and learned nonlinear features.

Scientific scope

Inherits FM, MLP and Wide&Deep principles; not invention of them. Reduced manual cross-feature design is not elimination of all preprocessing or a guarantee that arbitrary high-order interactions are exactly learned. CTR benchmark gains do not establish causal effects or long-term user welfare.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

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

Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.

Original sources