1 citations · 1 across the 22 of their papers we have counts for
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Adaptive Domain Interest Network for Multi-domain Recommendation
Yuchen Jiang, Qi Li, Han Zhu +5
Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously. In the ret…
WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Jingwei Zhuo, Bin Liu, Xiang Li +2
Learning the user-item relevance hidden in implicit feedback data plays an important role in modern recommender systems. Neural sequential recommendation models, which formulates l…
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation
Rihan Chen, Bin Liu, Han Zhu +8
Model-based methods for recommender systems have been studied extensively for years. Modern recommender systems usually resort to 1) representation learning models which define use…
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration
Siguang Huang, Yunli Wang, Lili Mou +4
Most machine learning classifiers only concern classification accuracy, while certain applications (such as medical diagnosis, meteorological forecasting, and computation advertisi…