8 papers
EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search
Shuwei Yuan, Mingqian Ding, Luxin Liu +2
E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulati…
SAM-D2Q: Aligning Multimodal Doc2Query with Search Demand and Conversion for E-commerce
Hui Zhou, Jian Hui Ji, Lei Ma +2
E-commerce search often suffers from vocabulary mismatch between user queries and merchant-authored product titles, since short titles cannot fully cover diverse user expressions o…
UniSGR: Unified Framework for Semantic ID Generation and Ranking
Jiawei Sun, Jun Yang, Ziyue Guo +4
Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-s…
Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation
Yantao Yu, Sen Qiao, Lei Shen +2
Recent progress in scaling large models has motivated recommender systems to increase model depth and capacity to better leverage massive behavioral data. However, recommendation i…
GateSID: Adaptive Gating for Balancing Semantic and Collaborative Signals in Recommendation
Hai Zhu, Yantao Yu, Lei Shen +2
In cold-start scenarios, the scarcity of collaborative signals for new items exacerbates the Matthew effect, undermining platform diversity and posing a persistent challenge in pra…
SORT: A Systematically Optimized Ranking Transformer for Industrial-scale Recommenders
Chunqi Wang, Bingchao Wu, Taotian Pang +9
While Transformers have achieved remarkable success in LLMs through superior scalability, their application in industrial-scale ranking models remains nascent, hindered by the chal…