2 citations · 4 across the 5 of their papers we have counts for
6 papers
Generative Recommenders Forget the Long Tail: Restoring Memorization in Retrieval and Ranking
Xu Huang, Jiancan Wu, Hongzheng Qin +5
Generative recommenders replace the retrieve-then-rank cascade with a single model: beam search over semantic item identifiers performs retrieval, and the same likelihood performs…
Joint Optimization of Multi-agent Memory System
Wenyu Mao, Haoyang Liu, Haosong Tan +4
Memory systems are critical for LLMs, mitigating context window limitations and supporting long-horizon user-LLM interactions. Such systems typically comprise multiple agents respo…
Fine-grained Semantics Integration for Large Language Model-based Recommendation
Jiawei Feng, Xiaoyu Kong, Leheng Sheng +8
Recent advances in Large Language Models (LLMs) have driven a shift in recommender systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommen…
MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation
Xiaoyu Kong, Leheng Sheng, Junfei Tan +5
The recent success of large language models (LLMs) has renewed interest in whether recommender systems can achieve similar scaling benefits. Conventional recommenders, dominated by…
Think before Recommendation: Autonomous Reasoning-enhanced Recommender
Xiaoyu Kong, Junguang Jiang, Bin Liu +6
The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent researc…
Reinforced Preference Optimization for Recommendation
Junfei Tan, Yuxin Chen, An Zhang +7
Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is ach…