arXiv AI July 21, 2026 By Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang, Mingjie Qian, Jinghui Qin, Liang Lin

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

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

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

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

TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning

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

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

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