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In recent years, large language models (LLM) have emerged as powerful toolsfor diverse natural language processing tasks. However, their potential forrecommender systems under the generative recommendation paradigm remainsrelatively unexplored. This paper presents an innovative approach torecommendation 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 itemto recommend, rather than calculating ranking score for each candidate item oneby one as in traditional discriminative recommendation. GenRec uses LLM'sunderstanding ability to interpret context, learn user preferences, andgenerate relevant recommendation. Our proposed approach leverages the vastknowledge encoded in large language models to accomplish recommendation tasks.We first we formulate specialized prompts to enhance the ability of LLM tocomprehend recommendation tasks. Subsequently, we use these prompts tofine-tune the LLaMA backbone LLM on a dataset of user-item interactions,represented by textual data, to capture user preferences and itemcharacteristics. Our research underscores the potential of LLM-based generativerecommendation in revolutionizing the domain of recommendation systems andoffers a foundational framework for future explorations in this field. Weconduct extensive experiments on benchmark datasets, and the experiments showsthat our GenRec has significant better results on large dataset.

GenRec: Large Language Model for Generative Recommendation