A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm release_vjvghekjqvdklnky5bmpghl75a

by Zhijun Zhang, Huali Pan, Gongwen Xu, Yongkang Wang, Pengfei Zhang

Published in Cybernetics and Information Technologies by Walter de Gruyter GmbH.

2016   Volume 16, p146-159

Abstract

<jats:title>Abstract</jats:title> 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.
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Date   2016-12-01
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