<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101,

Business School, Shandong Normal University, Jinan 250014,

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm"}

With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a...

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Pubblicato online: 25 gen 2017

Pagine: 146 - 159

DOI: https://doi.org/10.1515/cait-2016-0084

Parole chiave
Location services, social network, personalized recommendation, user context, social computing

© 2016 Zhijun Zhang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Pubblicato online: 25 gen 2017

Pagine: 146 - 159

DOI: https://doi.org/10.1515/cait-2016-0084

Parole chiaveLocation services, social network, personalized recommendation, user context, social computing

© 2016 Zhijun Zhang et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Location services, social network, personalized recommendation, user context, social computing