65 citations · 205 across the 14 of their papers we have counts for
4 papers · 1 filter
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Fangye Wang, Yingxu Wang, Dongsheng Li +4
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representat…
Parameter-free Dynamic Graph Embedding for Link Prediction
Jiahao Liu, Dongsheng Li, Hansu Gu +3
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for li…
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
Fangye Wang, Yingxu Wang, Dongsheng Li +4
CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation…
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
Chao Chen, Dongsheng Li, Junchi Yan +1
Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms…