Standard search engine results often fail to provide useful and reliable information for creating ideal travel itineraries. To address this issue, a method for generating personalized travel route recommendations using a knowledge map is introduced. This approach begins by understanding the specific desires of individual travellers. Then, it utilizes the structure and information within a tourism-focused knowledge map to design suitable routes. By merging the knowledge map with a recommendation system, the system's ability to provide relevant suggestions is improved. The validity and effectiveness of this personalized recommendation strategy are evaluated by testing it with real-world travel routes. Performance metrics, including hit rate and average reciprocal ranking, are calculated using actual tourism data. The findings demonstrate that this method effectively considers various aspects of personalized travel preferences and outperforms comparable algorithms. This paper addresses the challenge of providing accurate personalized travel recommendations by presenting a user- oriented system that leverages a tourism knowledge graph. The system constructs a knowledge graph from multi-source data, integrating it with user profiles to offer tailored recommendations. Compared to traditional systems, experimental results demonstrate that our system significantly improves recommendation accuracy (94.9% average) and user satisfaction (9.6 average score).

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Travel knowledge bases individualized recommendations travel path generation group-based filtering knowledge based graph