DUET pre-trains dedicated transformers for click and conversion streams, yielding up to 0.38% NE reduction over baselines in OCVR prediction.
InProceedings of the 17th ACM Conference on Recommender Systems (RecSys ’23)
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Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.
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DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
DUET pre-trains dedicated transformers for click and conversion streams, yielding up to 0.38% NE reduction over baselines in OCVR prediction.
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HiMARS: Hybrid multi-objective algorithms for recommender systems
Hybrid multi-objective algorithms inspired by NNIA, AMOSA, and NSGA-II generate Pareto-optimal recommendation lists that improve both accuracy and diversity over standard methods on real datasets.