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

ABSTRACT:

Introduction

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.

Methods

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 Lo

SUBMITTER: Juan Z 

PROVIDER: S-EPMC11628496 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature