arXiv AI

HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation

arXiv:2607. 17461v1 Announce Type: cross Abstract: The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.

arXiv AI

Jul 22

Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation

arXiv:2607. 18609v1 Announce Type: cross Abstract: The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities.

By Yongsen Zheng, Ruilin Xu, Guohua Wang, Liang Lin, Kwok-Yan Lam

benchmarks

arXiv AI

1d ago

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.

By Vahid Azizi, Fatemeh Koochaki

llms rag fine-tuning

arXiv AI

1d ago

TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning

arXiv:2608. 15055v1 Announce Type: new Abstract: Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide rich semantic understanding from textual attributes.

By David Yoon Suk Kang, JungHyun Kim, Juhyun Jeon, Sang-Wook Kim

llms benchmarks

arXiv AI

Aug 11

TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation

arXiv:2608. 09605v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for improving recommendation systems.

By Wenqiao Zhu, Chao Xu, Haipang Wu, Ji Liu

llms

arXiv Machine Learning

Jun 24

Macro Graph of Experts for Billion-Scale Multi-Task Recommendation

arXiv:2506. 10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs.

By Hongyu Yao, Zijin Hong, Hao Chen, Zhiqing Li, Qijie Shen, Zuobin Ying, Qihua Feng, Huan Gong, Feiran Huang

rag benchmarks

arXiv Machine Learning

Jun 5

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling

arXiv:2604. 12110v2 Announce Type: replace Abstract: Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity.

By Zikun Liu, Liang Luo, Qianru Li, Zhengyu Zhang, Wei Ling, Jingyi Shen, Zeliang Chen, Yaning Huang, Jingxian Huang, Abdallah Aboelela, Chonglin Sun, Feifan Gu, Fenggang Wu, Hang Qu, Huayu Li, Jill Pan, Kaidi Pei, Laming Chen, Longhao Jin, Qin Huang, Tongyi Tang, Varna Puvvada, Wenlin Chen, Xiaohan Wei, Xu Cao, Yantao Yao, Yuan Jin, Yunchen Pu, Yuxin Chen, Zijian Shen, Zhengkai Zhang, Jing Zhu, Dong Liang, Ellie Wen

rag efficiency

arXiv AI

Jun 3

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.

By Yuecheng Li, Zeyu Song, Jing Yao, Chi Lu, Peng Jiang, Kun Gai

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 17

On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies

arXiv:2606. 17276v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising direction for recommender systems.

By Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju, Donald Loveland, Bhuvesh Kumar, Kijung Shin, Neil Shah, Liam Collins

llms fine-tuning

arXiv AI

Aug 10

Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation

arXiv:2608. 06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.

By Ziyun Xu, Bosen Ding, Yue Zhang, Ji Qi, Qingyuan Song, Jizhou Huang, Liwei Wang, Jefferey Santelli, Yue Weng, Qichao Que, Zhenheng Yang, Junfeng Pan, Linhong Zhu

llms agents reinforcement-learning efficiency multimodal safety

arXiv AI

Jul 29

MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation

arXiv:2607. 25420v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pre-trained backbones alone when they are placed inside a structured recommendation pipeline.

By Jiahao Tian, Zhenkai Wang

llms agents benchmarks

arXiv AI

Jul 28

CALMRec: Causally Aligned Language Memory for Long-Horizon Recommendation

arXiv:2607. 23647v1 Announce Type: cross Abstract: Large language models (LLMs) can summarize heterogeneous user evidence in natural language, but current LLM recommenders often collapse enduring preferences, transient intent, and exposure-induced behavior into one profile.

By Gengyu Zhan

llms multimodal benchmarks safety

arXiv AI

Jul 7

MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.

By Ranxu Zhang, Junjie Meng, Ying Sun, Ziqi Xu, Bing Yin, Hao Li, Yanyong Zhang, Chao Wang

efficiency safety

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