3 papers
cs.IR2026
Cascading Relevance-driven Recommendation Network for CTR Prediction in Trigger-Introduced Recommendation
Kaixuan Chen, Wenwen Wang, Xing Fang +2
E-commerce has emerged as crucial platforms for people's daily consumption and shopping interests. There is a new recommendation scenario, Trigger-Introduced Recommendation (TIR),…
cs.IR2026
Learning to Forget: Satiation-Aware Long-Sequence Transducers for Mitigating Post-Purchase Redundancy
Yipin Dai, Ruocong Tang, Xing Fang +4
Sequential recommendation models predominantly interpret user interactions as positive signals for preference accumulation. However, in e-commerce scenarios, a purchase action ofte…
cs.IR2026
Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System
Ruocong Tang, Yang Huang, Xing Fang +3
Post-click conversion rate (CVR) is a crucial element in online recommendation systems, which addresses significant challenges such as data sparsity (DS), sample selection bias (SS…