Neural Collaborative Filtering: learned user–item interaction functions

What changed

Generalized latent-factor collaborative filtering by learning user–item interaction functions with neural networks, including matrix-factorization and MLP-based components.

Scientific scope

Generalized latent-factor collaborative filtering by learning user–item interaction functions with neural networks, including matrix-factorization and MLP-based components. WWW2017April publication precedes August arxivdeposit. Inherits collaborative filtering/deep models; observed/unobserved interactions are noisy implicit feedback, not true like/dislike labels. Original comparisons are not universal superiority over well-tuned matrix factorization.

Evidence

The linked original sources describe this contribution and its evidence limits. WRS has not independently reconstructed the full scientific claims. WWW2017April publication precedes August arxivdeposit. Inherits collaborative filtering/deep models; observed/unobserved interactions are noisy implicit feedback, not true like/dislike labels. Original comparisons are not universal superiority over well-tuned matrix factorization.

Contributors

• Source-listed authors and technical contributors · SCIENCE: 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.

• Neural Collaborative Filtering

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