  In recent years, large language models (LLM) have emerged as powerful tools
for diverse natural language processing tasks. However, their potential for
recommender systems under the generative recommendation paradigm remains
relatively unexplored. This paper presents an innovative approach to
recommendation systems using large language models (LLMs) based on text data.
In this paper, we present a novel LLM for generative recommendation (GenRec)
that utilized the expressive power of LLM to directly generate the target item
to recommend, rather than calculating ranking score for each candidate item one
by one as in traditional discriminative recommendation. GenRec uses LLM's
understanding ability to interpret context, learn user preferences, and
generate relevant recommendation. Our proposed approach leverages the vast
knowledge encoded in large language models to accomplish recommendation tasks.
We first we formulate specialized prompts to enhance the ability of LLM to
comprehend recommendation tasks. Subsequently, we use these prompts to
fine-tune the LLaMA backbone LLM on a dataset of user-item interactions,
represented by textual data, to capture user preferences and item
characteristics. Our research underscores the potential of LLM-based generative
recommendation in revolutionizing the domain of recommendation systems and
offers a foundational framework for future explorations in this field. We
conduct extensive experiments on benchmark datasets, and the experiments shows
that our GenRec has significant better results on large dataset.
