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CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation
Recommender systems are designed to learn user preferences from observed feedback and comprise many fundamental tasks, such as rating prediction and post-click conversion rate (pCVR) prediction.
Query-dominant User Interest Network for Large-Scale Search Ranking
The overall historical behaviors are various but noisy while search behaviors are always sparse.
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