  Recommendation systems have become an important solution to information
search problems. This article proposes a neural matrix factorization
recommendation system model based on the multimodal large language model called
BoNMF. This model combines BoBERTa's powerful capabilities in natural language
processing, ViT in computer in vision, and neural matrix decomposition
technology. By capturing the potential characteristics of users and items, and
after interacting with a low-dimensional matrix composed of user and item IDs,
the neural network outputs the results. recommend. Cold start and ablation
experimental results show that the BoNMF model exhibits excellent performance
on large public data sets and significantly improves the accuracy of
recommendations.
