A Personalized Recommendation Method for Tourist Attractions

Abstract

The invention discloses a personalized recommendation method for tourist attractions. Firstly, the original data set is used to construct the user knowledge graph and the scenic spot knowledge graph; and then the feature learning is performed on the user knowledge graph to obtain the first user representation vector and the first item representation vector ; Then, based on the first user representation vector, feature learning is carried out to the scenic spot knowledge map, and the second user representation vector and the second item representation vector are obtained; then the first user representation vector and the second user representation vector are combined into the final user representation vector; and The second item representation vector is directly used as the final item representation vector; finally, the final user representation vector and the final item representation vector are interacted in depth to predict the user's preference probability for scenic spots, thereby completing the personalized recommendation of tourist attractions. The present invention can avoid falling into the feature learning of a single knowledge map, so as to improve the accuracy of recommendation.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, in particular to a personalized recommendation method for tourist attractions. Background technique [0002] With the development of information technology and the Internet industry, especially the rise of electronic payment, users choose more and more platforms, and travel, travel booking methods and consumption methods are also changing. After service-oriented industries embrace digitalization and smart technology, major platforms have launched smart solutions to promote the digital upgrade of the industry. The data volume of major industries has grown rapidly, and information overload has become a challenge for people to process information. For specific users, how to quickly and accurately locate the content they need in the exponentially growing resources is a very important and extremely challenging matter. For service providers, how to present appropriate products to u...

AI Technical Summary

Problems solved by technology

[0004] Although the personalized recommendation algorithm has been developed for more than ten years, researchers are still working on exploring more efficient recommendation algorithms, but the current personalized recommendation technology still has some imperfections. From the perspective of users, Consider user behavior information from multiple perspectives, refine product categories, focus more on timeliness in recommendation results, higher quality recommendation results, more diverse recommendation content, and more accurate prediction results.

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