4 papers
Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
Zhijian Feng, Wenhao Zheng, Xuanji Xiao
In real-world recommendation systems, users would engage in variety scenarios, such as homepages, search pages, and related recommendation pages. Each of these scenarios would refl…
STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation
Wanda Li, Wenhao Zheng, Xuanji Xiao +1
Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users. As directly optimizing user retention is challeng…
Breaking the Curse of Knowledge: Towards Effective Multimodal Recommendation using Knowledge Soft Integration
Kai Ouyang, Chen Tang, Zenghao Chai +4
A critical challenge in contemporary recommendation systems lies in effectively leveraging multimodal content to enhance recommendation personalization. Although various solutions…
Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation
Kai Ouyang, Wenhao Zheng, Chen Tang +2
Post-click Conversion Rate (CVR) prediction task plays an essential role in industrial applications, such as recommendation and advertising. Conventional CVR methods typically suff…