TokenFormer unifies multi-field and sequential recommendation modeling via bottom-full-top-sliding attention and non-linear interaction representations to avoid sequential collapse and deliver state-of-the-art performance.
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DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.
MA-DNN augments DNNs with per-user memory vectors capturing likes and dislikes to exploit historical behavior for CTR prediction while remaining simpler than RNNs.
citing papers explorer
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TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds
TokenFormer unifies multi-field and sequential recommendation modeling via bottom-full-top-sliding attention and non-linear interaction representations to avoid sequential collapse and deliver state-of-the-art performance.
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Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.
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Click-Through Rate Prediction with the User Memory Network
MA-DNN augments DNNs with per-user memory vectors capturing likes and dislikes to exploit historical behavior for CTR prediction while remaining simpler than RNNs.