52 citations · 96 across the 12 of their papers we have counts for
5 papers · 1 filter
Hybrid Interest Modeling for Long-tailed Users
Lifang Deng, Jin Niu, Angulia Yang +4
User behavior modeling is a key technique for recommender systems. However, most methods focus on head users with large-scale interactions and hence suffer from data sparsity issue…
Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
Yuting Chen, Yanshi Wang, Yabo Ni +2
Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users…
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction
Yanshi Wang, Jie Zhang, Qing Da +1
Estimating post-click conversion rate (CVR) accurately is crucial in E-commerce. However, CVR prediction usually suffers from three major challenges in practice: i) data sparsity:…
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
Jianxiong Wei, Anxiang Zeng, Yueqiu Wu +3
The slate re-ranking problem considers the mutual influences between items to improve user satisfaction in e-commerce, compared with the point-wise ranking. Previous works either d…
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao +8
Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collecte…