17 citations · 21 across the 5 of their papers we have counts for
4 papers · 1 filter
Recommender Transformers with Behavior Pathways
Zhiyu Yao, Xinyang Chen, Sinan Wang +4
Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behav…
MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation
Linhao Luo, Yumeng Li, Buyu Gao +7
Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendat…
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction
Ze Meng, Jinnian Zhang, Yumeng Li +3
Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising a…
Learning User Representations with Hypercuboids for Recommender Systems
Shuai Zhang, Huoyu Liu, Aston Zhang +6
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifical…