A Deep Hybrid Recommendation Method for Multimodal Information Integrating Content Generated by Large Language Models

Item description information plays a crucial role in helping users understand the basic situation of an item and is also vital auxiliary information in recommendation systems. Traditional methods obtain this data through platform backend data or web scraping techniques, but these data are often static, relatively fixed, and insufficiently descriptive. In recent years, large language models (LLMs) like generative pre-trained transformer (GPT) have become powerful tools in natural language processing, bringing new hope for LLM-based recommendations. However, does the text information generated by large language models help improve recommendation accuracy? How can the information produced by generative artificial intelligence be integrated with existing multi-source heterogeneous information? In this paper, we propose a novel deep hybrid recommendation method for multimodal information integrating content generated by large language models (DML). We first explore the use of large language models to generate detailed descriptive information about movies. Next, we perform a weighted fusion of the generated text information with existing movie category information and user demographic data, among other multi-source heterogeneous information. Finally, we use the fused information to predict movie ratings. The results indicate that the multimodal information deep hybrid recommendation method, which integrates content generated by large language models, provides substantial evidence of superior performance relative to existing baseline models.