197 citations · 218 across the 4 of their papers we have counts for
5 papers · 1 filter
Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction
Qiwei Chen, Yue Xu, Changhua Pei +3
Recent studies on Click-Through Rate (CTR) prediction has reached new levels by modeling longer user behavior sequences. Among others, the two-stage methods stand out as the state-…
End-to-End User Behavior Retrieval in Click-Through RatePrediction Model
Qiwei Chen, Changhua Pei, Shanshan Lv +3
Click-Through Rate (CTR) prediction is one of the core tasks in recommender systems (RS). It predicts a personalized click probability for each user-item pair. Recently, researcher…
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao +8
As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior appro…
Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
Wenhui Yu, Xiao Lin, Junfeng Ge +2
Data sparsity is an inherent challenge in the recommender systems, where most of the data is collected from the implicit feedbacks of users. This causes two difficulties in designi…
Privileged Features Distillation at Taobao Recommendations
Chen Xu, Quan Li, Junfeng Ge +7
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the…