110 citations · 114 across the 8 of their papers we have counts for
6 papers · 1 filter
Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA
Teng Chen, Sheng Xu, Feixiang Guo +4
Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language models, incurring substantial…
Large Language Model Can Be a Foundation for Hidden Rationale-Based Retrieval
Luo Ji, Feixiang Guo, Teng Chen +9
Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and posi…
Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
Luo Ji, Gao Liu, Mingyang Yin +2
Modern listwise recommendation systems need to consider both long-term user perceptions and short-term interest shifts. Reinforcement learning can be applied on recommendation to s…
An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation
Luo Ji, Jiayu Mao, Hailong Shi +3
Online to offline recommendation strongly correlates with the user and service's spatiotemporal information, therefore calling for a higher degree of model personalization. The tra…
Deep Unified Representation for Heterogeneous Recommendation
Chengqiang Lu, Mingyang Yin, Shuheng Shen +3
Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…
Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation
Luo Ji, Qin Qi, Bingqing Han +1
Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challengin…