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A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Cybernetics and Information Technologies's Cover Image
Cybernetics and Information Technologies
Special issue with selection of extended papers from 6th International Conference on Logistic, Informatics and Service Science LISS’2016


With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, Information Technology