mlsanityData to 7 Oct 2026

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

arXiv 2407.08942 ↗First posted 12 Jul 2024cs.IR cs.AIcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 12 Jul 2024.

Builds on

10 of 25 references.

References · 10

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2017Neural Collaborative Filteringcited by 1,108
  4. 2015Neural Network Matrix Factorizationcited by 25
  5. 2024A Multimodal Fusion Network For Student Emotion Recognition Based on Transformer and Tensor Productcited by 22
  6. 2024Utilizing the LightGBM Algorithm for Operator User Credit Assessment Researchcited by 11
  7. 2024Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatmentcited by 11
  8. 2023An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithmscited by 5
  9. 2024Improved YOLOv5 Based on Attention Mechanism and FasterNet for Foreign Object Detection on Railway and Airway trackscited by 4
  10. 2014Promoting cold-start items in recommender systemscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 5; 2026: 1.

12 papers.

  1. 2025From Mind to Machine: The Rise of Manus AI as a Fully Autonomous Digital Agentcited by 33
  2. 2024Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learningcited by 19
  3. 2024AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Waycited by 15
  4. 2024Enhanced Recommendation Combining Collaborative Filtering and Large Language Modelscited by 10
  5. 2025Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacycited by 9
  6. 2025LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversioncited by 7
  7. 2024Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)cited by 4
  8. 2024Detecting and Classifying Defective Products in Images Using YOLOcited by 2
  9. 2025DeepRAG: Integrating Hierarchical Reasoning and Process Supervision for Biomedical Multi-Hop QAcited by 2
  10. 2024Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Modelscited by 1
  11. 2025Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendationcited by 0
  12. 2026DReX: An Explainable Deep Learning-based Multimodal Recommendation Frameworkcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
  2. 2026MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation
  3. 2025MMSRARec: Summarization and Retrieval Augumented Sequential Recommendation Based on Multimodal Large Language Model
  4. 2025Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation
  5. 2025Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
  6. 2026A General Framework for Multimodal LLM-Based Multimedia Understanding in Large-Scale Recommendation Systems
  7. 2026VLM4Rec: Multimodal Semantic Representation for Recommendation with Large Vision-Language Models
  8. 2025Bridging Domain Gaps between Pretrained Multimodal Models and Recommendations