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Multi-modal Bayesian Recommendation System

conference contribution
posted on 2024-09-19, 11:19 authored by Jie He, Lei Zhang, WenMing Cao, Mingming Yang, Man Li, Zihao Zhao, Man-Fai Leung

In this work, we introduce a Multi-modal Bayesian Recommendation System (MBR) that leverages both image and text modalities to enhance recommendation quality using common implicit feedback. Utilizing deep convolutional neural networks for image feature extraction and a language representation model for textual analysis, our proposed method effectively integrates these modalities to understand their collective impact on user preferences. Experiments on a large-scale dataset demonstrate the efficacy of MBR in providing improved recommendations.

History

Refereed

  • Yes

Publisher

IEEE

ISBN

2693-2776

Conference proceeding

2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)

Name of event

2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC)

Event start date

2024-05-24

Event finish date

2024-05-26

File version

  • Published version

Affiliated with

  • School of Computing and Information Science Outputs

Note

“© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

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