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cs.IR2026
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
Shijun Li, Wooseong Yang, Yu Wang +2
Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and natural-language reasoning abi…
cs.IR2026
R3-REC: Reasoning-Driven Recommendation via Retrieval-Augmented LLMs over Multi-Granular Interest Signals
Yuchen Miao, Mingxuan Cui, Yitong Zhu +2
This paper addresses two persistent challenges in sequential recommendation: (i) evidence insufficiency-cold-start sparsity together with noisy, length-varying item texts; and (ii)…
cs.IR2025
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation
Hamed Firooz, Maziar Sanjabi, Adrian Englhardt +20
Ranking and recommendation systems are the foundation for numerous online experiences, ranging from search results to personalized content delivery. These systems have evolved into…