2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.IR2023
FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation
Ming Li, Naiyin Liu, Xiaofeng Pan +5
Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely o…
cs.IR2022★ 2 cited
Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations
Ningning Li, Qunwei Li, Xichen Ding +2
Deep Candidate Generation plays an important role in large-scale recommender systems. It takes user history behaviors as inputs and learns user and item latent embeddings for candi…