  Pre-trained models learn general representations from large datsets which can
be fine-turned for specific tasks to significantly reduce training time.
Pre-trained models like generative pretrained transformers (GPT), bidirectional
encoder representations from transformers (BERT), vision transfomers (ViT) have
become a cornerstone of current research in machine learning. This study
proposes a multi-modal movie recommendation system by extract features of the
well designed posters for each movie and the narrative text description of the
movie. This system uses the BERT model to extract the information of text
modality, the ViT model applied to extract the information of poster/image
modality, and the Transformer architecture for feature fusion of all modalities
to predict users' preference. The integration of pre-trained foundational
models with some smaller data sets in downstream applications capture
multi-modal content features in a more comprehensive manner, thereby providing
more accurate recommendations. The efficiency of the proof-of-concept model is
verified by the standard benchmark problem the MovieLens 100K and 1M datasets.
The prediction accuracy of user ratings is enhanced in comparison to the
baseline algorithm, thereby demonstrating the potential of this cross-modal
algorithm to be applied for movie or video recommendation.
