20 citations · 71 across the 35 of their papers we have counts for
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cs.IR2024
Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Shengyu Zhang, Ziqi Jiang, Jiangchao Yao +7
Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. W…
cs.IR2021★ 3 cited
Click-through Rate Prediction with Auto-Quantized Contrastive Learning
Yujie Pan, Jiangchao Yao, Bo Han +3
Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scena…
cs.IR2021★ 6 cited
MC-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
Zeyuan Chen, Jiangchao Yao, Feng Wang +4
With the hardware development of mobile devices, it is possible to build the recommendation models on the mobile side to utilize the fine-grained features and the real-time feedbac…