bias mitigation 1click-through rate prediction 1e-commerce search 1feature tokenization 1transformer ranking models 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.IR2026
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…
cs.IR2026
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…
cs.IR2026
Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
Jianbo Zhu, Xing Fang, Jing Wang +5
Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However, its practical adoption in indu…