  Recommendation systems for Web content distribution intricately connect to
the information access and exposure opportunities for vulnerable populations.
The emergence of Large Language Models-based Recommendation System (LRS) may
introduce additional societal challenges to recommendation systems due to the
inherent biases in Large Language Models (LLMs). From the perspective of
item-side fairness, there remains a lack of comprehensive investigation into
the item-side fairness of LRS given the unique characteristics of LRS compared
to conventional recommendation systems. To bridge this gap, this study examines
the property of LRS with respect to item-side fairness and reveals the
influencing factors of both historical users' interactions and inherent
semantic biases of LLMs, shedding light on the need to extend conventional
item-side fairness methods for LRS. Towards this goal, we develop a concise and
effective framework called IFairLRS to enhance the item-side fairness of an
LRS. IFairLRS covers the main stages of building an LRS with specifically
adapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS
to fine-tune LLaMA, a representative LLM, on \textit{MovieLens} and
\textit{Steam} datasets, and observe significant item-side fairness
improvements. The code can be found in
https://github.com/JiangM-C/IFairLRS.git.
