  Recent advancements in recommendation systems have shifted towards more
comprehensive and personalized recommendations by utilizing large language
models (LLM). However, effectively integrating LLM's commonsense knowledge and
reasoning abilities into recommendation systems remains a challenging problem.
In this paper, we propose RecSysLLM, a novel pre-trained recommendation model
based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating
recommendation domain knowledge through unique designs of data, training, and
inference. This allows RecSysLLM to leverage LLMs' capabilities for
recommendation tasks in an efficient, unified framework. We demonstrate the
effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM
provides a promising approach to developing unified recommendation systems by
fully exploiting the power of pre-trained language models.
