15 citations · 17 across the 6 of their papers we have counts for
6 papers
AliBoost: Ecological Boosting Framework in Alibaba Platform
Qijie Shen, Yuanchen Bei, Zihong Huang +8
Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. Th…
Feedback Reciprocal Graph Collaborative Filtering
Weijun Chen, Yuanchen Bei, Qijie Shen +3
Collaborative filtering on user-item interaction graphs has achieved success in the industrial recommendation. However, recommending users' truly fascinated items poses a seesaw di…
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Hao Chen, Yuanchen Bei, Qijie Shen +6
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational comp…
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Yue Xu, Hao Chen, Zefan Wang +8
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed produc…
Multi-channel Integrated Recommendation with Exposure Constraints
Yue Xu, Qijie Shen, Jianwen Yin +6
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Thoug…
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
Qijie Shen, Hong Wen, Jing Zhang +1
Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due…