# A Neural Matrix Decomposition Recommender System Model based on the Multimodal Large Language Model

arXiv: https://arxiv.org/abs/2407.08942 · HTML: https://mlsanity.com/paper/2407.08942/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 12 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Neural Network Matrix Factorization](https://mlsanity.com/paper/1511.06443/) (2015)
- [A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Product](https://mlsanity.com/paper/2403.08511/) (2024)
- [Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research](https://mlsanity.com/paper/2403.14483/) (2024)
- [Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment](https://mlsanity.com/paper/2404.12634/) (2024)
- [An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms](https://mlsanity.com/paper/2310.16430/) (2023)
- [Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway tracks](https://mlsanity.com/paper/2403.08499/) (2024)
- [Promoting cold-start items in recommender systems](https://mlsanity.com/paper/1404.4936/) (2014)

## Built on by (most cited first, 12 of 12)

- [From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agent](https://mlsanity.com/paper/2505.02024/) (2025)
- [Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning](https://mlsanity.com/paper/2408.16633/) (2024)
- [AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way](https://mlsanity.com/paper/2409.14577/) (2024)
- [Enhanced Recommendation Combining Collaborative Filtering and Large Language Models](https://mlsanity.com/paper/2412.18713/) (2024)
- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2411.18314/) (2024)
- [Detecting and Classifying Defective Products in Images Using YOLO](https://mlsanity.com/paper/2412.16935/) (2024)
- [DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QA](https://mlsanity.com/paper/2506.00671/) (2025)
- [Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models](https://mlsanity.com/paper/2412.18715/) (2024)
- [Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation](https://mlsanity.com/paper/2506.10658/) (2025)
- [DReX: An Explainable Deep Learning-based Multimodal Recommendation Framework](https://mlsanity.com/paper/2602.19702/) (2026)

## Abstract (arXiv)

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.