UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories. Production systems couple them only loosely. To unify the two, we present UniDot, a novel architecture for post-click conversion prediction built from the factorization-machine (FM) point of view: the embedding inner product---which powers collaborative filtering and lets a recommender generalize to unseen user--item pairs---is the same primitive as attention's query dot key scoring, so a
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- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Rongcheng Lin →
“UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation”
- LinkedLinked via arxiv author · 85%Yan Sun →
“UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation”
- LinkedLinked via arxiv author · 85%Jamey Zhang →
“UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation”
- LinkedLinked via arxiv author · 85%Guanglei Xiong →
“UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation”
- LinkedLinked via arxiv author · 85%Ivan Ji →
“UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation”