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

In recent years, tourism revenue and visitor numbers in Northwest China have increased steadily. However, many tourists still have limited knowledge of scenic destinations across the five northwestern provinces. When travelers intend to visit the region but have not yet decided on specific destinations, an intelligent recommendation system is urgently needed to assist their decision-making. Based on collaborative filtering, content matching, or knowledge graphs existing systems primarily face three major challenges: Due to reliance on historical data, the recommendation performance for new users and new attractions is weak; limited ability to capture tourists' current intentions and personalized needs; insufficient utilization of multimodal information. To address these challenges, We propose a novel deep learning-based multimodal recommendation model, T-ECBM. A dataset comprising 23,488 user reviews and 4160 images of 52 attractions was collected. BERT was employed to extract semantic features from reviews, capturing subjective preferences and sentiment, while an improved EfficientNet-CA model extracted visual features from images to identify key scenic elements. The two feature sets were fused and fed into a multilayer perceptron, formulating the recommendation task as a multi-class classification problem. Experimental results demonstrate that text-only BERT achieved a Top-1 accuracy of 82.67%, while image-only EfficientNet-CA reached 83.68%. In contrast, the proposed T-ECBM achieved 96.71% Top-1 accuracy, 99.82% Top-5 accuracy, and an F1-score of 96.70%, proving its significant superiority over unimodal approaches. By integrating textual and visual modalities, T-ECBM effectively reduces information asymmetry, enriches decision-making support, and delivers intelligent, efficient, and personalized recommendations for tourists exploring northwestern China.