What changed
Used candidate-conditioned attention to select historical user interests; DIEN added auxiliary-supervised sequential interest extraction and attention-modulated recurrent evolution.
Scientific scope
Used candidate-conditioned attention to select historical user interests; DIEN added auxiliary-supervised sequential interest extraction and attention-modulated recurrent evolution. 2017DIN and2018DIEN bundled with different original bylines. Inherits attention/GRUs and behavior embeddings; inferred latent interest is not measured psychological state or causal preference. v1DIN does not acquire later Dice/minibatch-regularization details without the corresponding revised source. Claimed online CTR changes are source-specific experiments, not universal welfare improvements.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 2017DIN and2018DIEN bundled with different original bylines. Inherits attention/GRUs and behavior embeddings; inferred latent interest is not measured psychological state or causal preference. v1DIN does not acquire later Dice/minibatch-regularization details without the corresponding revised source. Claimed online CTR changes are source-specific experiments, not universal welfare improvements.
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.
- Deep Interest Network for Click-Through Rate Prediction
- Deep Interest Evolution Network for Click-Through Rate Prediction
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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