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.
@article{arxiv.2407.08942,
title = {A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model},
author = {Ao Xiang and Bingjie Huang and Xinyu Guo and Haowei Yang and Tianyao Zheng},
journal= {arXiv preprint arXiv:2407.08942},
year = {2024}
}