Location Identification and Personalized Recommendation of Tourist Attractions Based on Image Processing

Currently, tourists tend to plan travel routes and itineraries by searching for relevant information on tourist attractions via the Internet and intelligent terminals. However, it is difficult to achieve good retrieval effect on tourist attraction images with text labels. Based on deep learning, the visual location identification faces such defects as frequent mismatching, high probability of weak matching, and long execution time. To solve these defects, this paper puts forward a novel method for location identification and personalized recommendation of tourist attractions based on image processing. Specifically, the authors detailed the ideas and steps of the location identification algorithm for tourist attractions. The algorithm, grounded on hash retrieval, encompasses two stages: an offline stage, and an online stage. Besides, a personalized recommendation model for tourist attractions based on geographical location and time period. Finally, the proposed algorithm and model were proved accurate and effective through experiments.

Automated summary

This paper proposes a novel method for location identification and personalized recommendation of tourist attractions based on image processing, which solves the problems of frequent mismatching, high probability of weak matching, and long execution time in existing methods through deep learning algorithms and hash retrieval in two stages.