A Research Method based on POI Similarity and Translation Machine Matching Recommendation Algorithm.

When recommendation algorithms are used in different languages and environments, the application of traditional recommendation algorithms will face many challenges. This study explores recommendation algorithms based on POI similarity and the matching method of translation machine to improve the accuracy and personalization of recommendations. This paper constructs a POI database including tourist attractions information, calculates the similarity between the attractions by similarity calculation methods, and utilizes translation machine technology to match the user language preferences with the attraction information. The recommendation algorithm designed by the paper is to use the results from POI similarity and translation machine matching to generate a personalized recommendation for tourist attractions. From the experimental evaluation, the recommendation algorithms based on POI similarity and translation machine matching have shown significant results in the recommendation, which can provide a more accurate and personalized recommendation results based on the user's interest and the language user prefers. [ABSTRACT FROM AUTHOR]