4 papers
LLM4AIGQ: LLM-based AI Guidance Query Generation Framework for Multi Interest Mining
Xiangchen Pan, Jiayi Xu, Jing Wang +2
Guidance queries stimulate user consumption by extracting preferences to provide search queries with guidance value, playing a crucial role in the e-commerce field. Traditional AI-…
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),…
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…
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…