2 citations · 2 across the 17 of their papers we have counts for
17 papers
Beyond Semantic IDs: Encoding Business-Value Ranking into Document Identifiers for Generative Retrieval
Gui Ling, Zhihong Chen, Yu Li +7
Generative Retrieval (GR) formulates retrieval as a sequence-to-sequence generation task, assigning each document a document identifier (DocID) and retrieving it through autoregres…
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…
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…
Canzona: A Unified, Asynchronous, and Load-Balanced Framework for Distributed Matrix-based Optimizers
Liangyu Wang, Siqi Zhang, Junjie Wang +7
The scaling of Large Language Models (LLMs) drives interest in matrix-based optimizers (e.g., Shampoo, Muon, SOAP) for their convergence efficiency; yet their requirement for holis…
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…
ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants
Pei Wang, Yanan Wu, Xiaoshuai Song +13
Large language model (LLM)-based agents are increasingly deployed in e-commerce shopping. To perform thorough, user-tailored product searches, agents should interpret personal pref…
Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation
Hanqi Jin, Gaoming Yang, Zhangming Chan +7
User interactions on e-commerce platforms are inherently diverse, involving behaviors such as clicking, favoriting, adding to cart, and purchasing. The transitions between these be…
DecisionLLM: Large Language Models for Long Sequence Decision Exploration
Xiaowei Lv, Zhilin Zhang, Yijun Li +10
Long-sequence decision-making, which is usually addressed through reinforcement learning (RL), is a critical component for optimizing strategic operations in dynamic environments,…
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…
Beyond Existing Retrievals: Cross-Scenario Incremental Sample Learning Framework
Tao Wang, Xun Luo, Jinlong Guo +4
The parallelized multi-retrieval architecture has been widely adopted in large-scale recommender systems for its computational efficiency and comprehensive coverage of user interes…
Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework
Ying Song, Yijing Wang, Hui Yang +12
Evaluating platform-level interventions in search-based two-sided marketplaces is fundamentally challenged by systemic effects such as spillovers and network interference. While wi…
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-…
GSID: Generative Semantic Indexing for E-Commerce Product Understanding
Haiyang Yang, Qinye Xie, Qingheng Zhang +7
Structured representation of product information is a major bottleneck for the efficiency of e-commerce platforms, especially in second-hand ecommerce platforms. Currently, most pr…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning
Jia Zhang, Chen-Xi Zhang, Yao Liu +5
Recent advancements in instruction tuning for large language models (LLMs) suggest that a small, high-quality dataset can significantly equip LLMs with instruction-following capabi…
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples
Shuo Xie, Fangzhi Zhu, Jiahui Wang +6
Aligning Large Language Models (LLMs) with human feedback is crucial for their development. Existing preference optimization methods such as DPO and KTO, while improved based on Re…