5 papers
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
Jiacheng Chen, Tao Zhang, Manxi Lin +26
The wave of AI-native applications is moving shopping beyond page- and feed-based browsing toward intent-driven experiences orchestrated by LLM agents. A common design wraps an LLM…
Prompt Generation Technical Report
Dan Ou, Gui Ling, Hao Wan +25
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing…
RankGR: Rank-Enhanced Generative Retrieval with Listwise Direct Preference Optimization in Recommendation
Kairui Fu, Changfa Wu, Kun Yuan +8
Generative retrieval (GR) has emerged as a promising paradigm in recommendation systems by autoregressively decoding identifiers of target items. Despite its potential, current app…
PI2I: A Personalized Item-Based Collaborative Filtering Retrieval Framework
Shaoqing Wang, Yingcai Ma, Kairui Fu +4
Efficiently selecting relevant content from vast candidate pools is a critical challenge in modern recommender systems. Traditional methods, such as item-to-item collaborative filt…
TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
Zida Liang, Changfa Wu, Dunxian Huang +9
Recommendation systems are essential tools in modern e-commerce, facilitating personalized user experiences by suggesting relevant products. Recent advancements in generative model…