10 papers
BehaviorMem: Building and Evaluating User Memory from Real-World E-Commerce Behavior Streams
Sizhe Wang, Xinghao Fang, Rui Qiu +4
User memory helps personalized systems build on their understanding of users over time. In e-commerce, users convey their preferences and needs through both explicit language and i…
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),…
SSR-GRPO: Integrating Supervision and Semantic IDs into Reinforcement Learning for Dense Retrieval in E-commerce
Guangxin Song, Xing Fang, Mingmin Jin +5
Embedding-based retrieval (EBR) is pivotal in e-commerce search but often struggles with complex semantics. While recent methods often fine-tune large language models (LLMs) for re…
TMallGS: Scaling Unified Feature and Sequence Modeling for Generative E-commerce Search
Zhentao Song, Yufeng Gao, Xing Fang +5
In industrial search and ranking systems, Click-Through Rate (CTR) prediction is shifting from traditional Deep Learning Recommendation Models (DLRM) toward unified, compute-intens…
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…
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…
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…