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

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arxiv 2407.08942 v1 pith:N5L66SOF submitted 2024-07-12 cs.IR cs.AI

Ao Xiang , Bingjie Huang , Xinyu Guo , Haowei Yang , Tianyao Zheng This is my paper · ORCID
classification cs.IRcs.AI
keywords modelmatrixneurallanguagelargebonmfdecompositionmultimodal
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network

    cs.CV 2024-12 reject novelty 4.0 of 10

    DTSGAN adapts SinGAN-style multi-scale generation to video with 3D convolutions and a sliding-window data update, claiming improved dynamic texture synthesis and diversity.

  2. Detecting and Classifying Defective Products in Images Using YOLO

    cs.CV 2024-12 reject novelty 2.0 of 10

    An unverifiable report that a YOLO variant with ResC2Net, SPPF, and PConv modules detects machine-part defects at mAP 0.91 without comparing to any baseline.

  3. Enhanced Recommendation Combining Collaborative Filtering and Large Language Models

    cs.AI 2024-12 reject novelty 1.0 of 10

    A simple weighted sum of collaborative filtering scores and LLM text embeddings is claimed to improve recommendation accuracy, but the reported experiments are not reproducible.

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