  The importance of recommender systems is growing rapidly due to the
exponential increase in the volume of content generated daily. This surge in
content presents unique challenges for designing effective recommender systems.
Key among these challenges is the need to effectively leverage the vast amounts
of natural language data and images that represent user preferences. This paper
presents a novel approach to enhancing recommender systems by leveraging Large
Language Models (LLMs) and deep learning techniques. The proposed framework
aims to improve the accuracy and relevance of recommendations by incorporating
multi-modal information processing and by the use of unified latent space
representation. The study explores the potential of LLMs to better understand
and utilize natural language data in recommendation contexts, addressing the
limitations of previous methods. The framework efficiently extracts and
integrates text and image information through LLMs, unifying diverse modalities
in a latent space to simplify the learning process for the ranking model.
Experimental results demonstrate the enhanced discriminative power of the model
when utilizing multi-modal information. This research contributes to the
evolving field of recommender systems by showcasing the potential of LLMs and
multi-modal data integration to create more personalized and contextually
relevant recommendations.
