From the 3 of 7 linked papers with an AI index.
7 papers
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),…
TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search
Zhentao Song, Yufeng Gao, Xing Fang +5
The paper introduces TMallGS, a transformer-based ranking architecture for e‑commerce search that combines specialized tokenization, field‑adaptive transformers, and bias‑aware tra…
Learning to Forget: Satiation-Aware Long-Sequence Transducers for Mitigating Post-Purchase Redundancy
Yipin Dai, Ruocong Tang, Xing Fang +4
The paper introduces a satiation-aware framework for sequential recommendation that detects when a purchase satisfies a user’s intent and temporarily suppresses related items, then…
Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System
Ruocong Tang, Yang Huang, Xing Fang +3
The paper introduces a Discount-Aware Network (DANet) that incorporates item discount information via Fourier-based time‑frequency analysis and bias‑mitigation modules to improve p…
DSIRM: Learning Query-Bridged Discrete Semantic Identifiers for E-commerce Relevance Modeling
Bokang Wang, Xing Fang, Mingmin Jin +4
Despite rapid progress of continuous embeddings for e-commerce search relevance, a long-standing open problem is the difficulty in capturing fine-grained attribute distinctions. Wh…
From Head to Tail: Asymmetric Knowledge Transfer in Long-tail Recommendation with Generative Semantic IDs
Chenyi Yan, Ruocong Tang, Xing Fang +3
Long-tail recommendation in real-world e-commerce platforms remains challenging due to severe data imbalance. Existing methods often struggle to combine content-based multimodal fe…