5 citations · 5 across the 6 of their papers we have counts for
6 papers
Probe-then-Plan: Environment-Aware Planning for Industrial E-commerce Search
Mengxiang Chen, Zhouwei Zhai, Jin Li
Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based paradigms face a fundamental bli…
GenFacet: End-to-End Generative Faceted Search via Multi-Task Preference Alignment in E-Commerce
Zhouwei Zhai, Min Yang, Jin Li
Faceted search acts as a critical bridge for navigating massive ecommerce catalogs, yet traditional systems rely on static rule-based extraction or statistical ranking, struggling…
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li +4
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequen…
Learning to Personalize Recommendation based on Customers' Shopping Intents
Xin Shen, Jiaying Shi, Sungro Yoon +4
Understanding the customers' high level shopping intent, such as their desire to go camping or hold a birthday party, is critically important for an E-commerce platform; it can hel…
Coarse-to-Fine Sparse Sequential Recommendation
Jiacheng Li, Tong Zhao, Jin Li +5
Sequential recommendation aims to model dynamic user behavior from historical interactions. Self-attentive methods have proven effective at capturing short-term dynamics and long-t…
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
Trong Nghia Hoang, Anoop Deoras, Tong Zhao +2
This paper studies the item-to-item recommendation problem in recommender systems from a new perspective of metric learning via implicit feedback. We develop and investigate a pers…