1 citations · 1 across the 8 of their papers we have counts for
8 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…
Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models
Xuanji Xiao, Ziyu He
Rank models play a key role in industrial recommender systems, advertising, and search engines. Existing works utilize semantic tags and user-item interaction behaviors, e.g., clic…
Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent
Xuanji Xiao, Huaqiang Dai, Qian Dong +3
Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge. Existing works predict the user interest by…
On Modeling Long-Term User Engagement from Stochastic Feedback
Guoxi Zhang, Xing Yao, Xuanji Xiao
An ultimate goal of recommender systems (RS) is to improve user engagement. Reinforcement learning (RL) is a promising paradigm for this goal, as it directly optimizes overall perf…
NCS4CVR: Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction
Xuanji Xiao, Huabin Chen, Yuzhen Liu +5
Click-through rate (CTR) and post-click conversion rate (CVR) predictions are two fundamental modules in industrial ranking systems such as recommender systems, advertising, and se…