Showing cs.IRShow all
2 papers · 1 filter
cs.IR2026
Every Preference Has Its Strength: Injecting Ordinal Semantics into LLM-Based Recommenders
Jiwon Jeong, Donghee Han, Sungrae Hong +2
Recent work has shown that large language models (LLMs) can enhance recommender systems by integrating collaborative filtering (CF) signals through hybrid prompting. However, most…
cs.IR2025
Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration
Donghee Han, Hwanjun Song, Mun Yong Yi
Recent studies have explored integrating large language models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from…