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Found 6 papers, 3 papers with code
Robust Representation Learning for Unified Online Top-K Recommendation
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
Adaptive Domain Interest Network for Multi-domain Recommendation
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously.
Context-aware Tree-based Deep Model for Recommender Systems
Vertically, a parent fusion layer is designed in M to transmit the user preference representation in higher levels of T to the current level, grasping the essence that tree-based methods are generating the candidate set from coarse to detail during the beam search retrieval.
Learning Optimal Tree Models Under Beam Search
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.
Predcnn: Predictive learning with cascade convolutions
Predicting future frames in videos remains an unsolved but challenging problem.
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