CASE improves next-basket repurchase recommendation by modeling item cadences as calendar-time signals with multi-scale convolutions and induced set attention, delivering up to 8.6% precision and 9.9% recall lifts at top-5 in production.
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PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
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CASE: Cadence-Aware Set Encoding for Large-Scale Next Basket Repurchase Recommendation
CASE improves next-basket repurchase recommendation by modeling item cadences as calendar-time signals with multi-scale convolutions and induced set attention, delivering up to 8.6% precision and 9.9% recall lifts at top-5 in production.
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Learning Shared Sentiment Prototypes for Adaptive Multimodal Sentiment Analysis
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.