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Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

T-ECBM: a deep learning-based text-image multimodal model for tourist attraction recommendation.

Jianfu Chen¹, Jiaxu Cong², Mingxiao Li¹

¹School of Intelligence Science and Engineering, Qinghai Minzu University, Xining, China.

Scientific Reports

|November 25, 2025

Summary

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A new deep learning model, T-ECBM, enhances travel recommendations for Northwest China by combining text and image data. This intelligent system improves destination discovery for tourists, overcoming limitations of existing recommendation approaches.

Area of Science:

Artificial Intelligence
Computer Science
Tourism Informatics

Background:

Northwest China sees growing tourism, but visitors lack destination knowledge.
Existing recommendation systems struggle with new users, current needs, and multimodal data.

Purpose of the Study:

To develop an intelligent, multimodal recommendation system for Northwest China tourism.
To address limitations of traditional recommendation models in capturing user preferences and utilizing diverse data.

Main Methods:

Proposed T-ECBM, a deep learning multimodal recommendation model.
Utilized BERT for text review analysis and EfficientNet-CA for image feature extraction.
Fused textual and visual features for a multi-class classification recommendation task.

Keywords:

Deep learning Feature integration Multimodal fusion Recommendation model Tourist attractions

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Main Results:

T-ECBM achieved 96.71% Top-1 and 99.82% Top-5 accuracy.
Outperformed text-only (82.67%) and image-only (83.68%) models significantly.
Demonstrated superior performance with an F1-score of 96.70%.

Conclusions:

T-ECBM effectively integrates multimodal information for superior tourist recommendations.
The model reduces information asymmetry and enhances decision-making for travelers.
Offers intelligent, efficient, and personalized travel guidance for Northwest China.