16 papers
Beyond Semantic IDs: Encoding Business-Value Ranking into Document Identifiers for Generative Retrieval
Gui Ling, Zhihong Chen, Yu Li +7
The paper proposes Cluster‑Ranked Identifier (CRID), a document ID design that separates semantic clustering from business‑value ranking to eliminate collisions and better align re…
E-VAds: An E-commerce Short Videos Understanding Benchmark for MLLMs
Xianjie Liu, Yiman Hu, Liang Wu +4
E-commerce short videos represent a high-revenue segment of the online video industry characterized by a goal-driven format and dense multi-modal signals. Current models often stru…
A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
Kai Li, Jintao Cheng, Chang Zeng +5
Query-based universal sound separation is fundamental to intelligent auditory systems, aiming to isolate specific sources from mixtures. Despite recent advances, existing methods c…
Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
Lei Chen, Chen Ju, Xu Chen +13
In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical chal…
Retrieval-GRPO: A Multi-Objective Reinforcement Learning Framework for Dense Retrieval in Taobao Search
Xingxian Liu, Dongshuai Li, Jiahui Wan +7
Dense retrieval, as the core component of e-commerce search engines, maps user queries and items into a unified semantic space through pre-trained embedding models to enable large-…
Zenith: Scaling up Ranking Models for Billion-scale Livestreaming Recommendation
Ruifeng Zhang, Zexi Huang, Zikai Wang +11
Accurately capturing feature interactions is essential in recommender systems, and recent trends show that scaling up model capacity could be a key driver for next-level predictive…