197 citations · 220 across the 9 of their papers we have counts for
9 papers
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…
Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System
Shanshan Lyu, Qiwei Chen, Tao Zhuang +1
Recommender system is an essential part of online services, especially for e-commerce platform. Conversion Rate (CVR) prediction in RS plays a significant role in optimizing Gross…
Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
Xu Yuan, Chen Xu, Qiwei Chen +3
In this era of information explosion, a personalized recommendation system is convenient for users to get information they are interested in. To deal with billions of users and ite…
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…