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.

翻译：推荐系统已成为解决信息检索问题的重要方案。本文提出了一种基于多模态大语言模型的神经矩阵分解推荐系统模型，称为BoNMF。该模型融合了BoBERTa在自然语言处理领域的强大能力、计算机视觉中的ViT技术以及神经矩阵分解方法。通过捕捉用户与项目的潜在特征，并使其与由用户和项目ID构成的低维矩阵进行交互后，神经网络输出推荐结果。冷启动与消融实验结果表明，BoNMF模型在大型公共数据集上表现出优异性能，并显著提升了推荐准确性。