A multimodal travel route recommendation system leveraging visual Transformers and self-attention mechanisms

With the rapid development of the tourism industry, the demand for accurate and personalized travel route recommendations has significantly increased. However, traditional methods often fail to effectively integrate visual and sequential information, leading to recommendations that are both less accurate and less personalized.

This paper introduces SelfAM-Vtrans, a novel algorithm that leverages multimodal data-combining visual Transformers, LSTMs, and self-attention mechanisms-to enhance the accuracy and personalization of travel route recommendations. SelfAM-Vtrans integrates visual and sequential information by employing a visual Transformer to extract features from travel images, thereby capturing spatial relationships within them. Concurrently, a Long Short-Term Memory (LSTM) network encodes sequential data to capture the temporal dependencies within travel sequences. To effectively merge these two modalities, a self-attention mechanism fuses the visual features and sequential encodings, thoroughly accounting for their interdependencies. Based on this fused representation, a classification or regression model is trained using real travel datasets to recommend optimal travel routes.

The algorithm was rigorously evaluated through experiments conducted on real-world travel datasets, and its performance was benchmarked against other route recommendation methods. The results demonstrate that SelfAM-Vtrans significantly outperforms traditional approaches in terms of both recommendation accuracy and personalization. By comprehensively incorporating both visual and sequential data, this method offers travelers more tailored and precise route suggestions, thereby enriching the overall travel experience.