Tourist attraction image recognition and intelligent recommendation based on deep learning

Abstract

The rapid development of the economy has also increased people’s purchasing power, and many young people choose to reward themselves with a trip. The inspiration for such trips often comes from diverse recommendations and sharing on the internet, and image recognition technology plays a key role in this process, helping users confirm the specific location of the shared content. However, the accuracy and adaptability of current online tourist attraction image recognition and recommendation systems are still insufficient, making it difficult to fully meet user needs. In response to this issue, this study adopted neural collaborative filtering technology to conduct in-depth research on the intelligent tourist attraction recommendation system. By constructing a deep convolutional neural network, this study automatically extracted advanced features from images and combined multimodal data fusion and user portrait analysis techniques to achieve high-precision recognition and customized recommendation of tourist attractions. Research shows that this method can maintain high image recognition precision, with a precision of 89%, which is significantly improved compared to traditional methods. In terms of recommending travel attractions, its precision has also reached a satisfactory 85%, proving its excellent performance and broad application potential. This study demonstrates the potential of neural collaborative filtering and deep convolutional neural networks in improving the accuracy and adaptability of tourist attraction image recognition and recommendation systems, offering a more personalized and efficient travel experience.