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http://dx.doi.org/10.7472/jksii.2018.19.3.7

A Social Travel Recommendation System using Item-based collaborative filtering  

Kim, Dae-ho (Dept. of Software, Gachon University)
Song, Je-in (Dept. of Software, Gachon University)
Yoo, So-yeop (Dept. of Software, Gachon University)
Jeong, Ok-ran (Dept. of Software, Gachon University)
Publication Information
Journal of Internet Computing and Services / v.19, no.3, 2018 , pp. 7-14 More about this Journal
Abstract
As SNS(Social Network Service) becomes a part of our life, new information can be derived through various information provided by SNS. Through the public timeline analysis of SNS, we can extract the latest tour trends for the public and the intimacy through the social relationship analysis in the SNS. The extracted intimacy can also be used to make the personalized recommendation by adding the weights to friends with high intimacy. We apply SNS elements such as analyzed latest trends and intimacy to item-based collaborative filtering techniques to achieve better accuracy and satisfaction than existing travel recommendation services in a new way. In this paper, we propose a social travel recommendation system using item - based collaborative filtering.
Keywords
Personalized Recommendation; Item-based Collaborative Filtering; Apache Mahout; Social Travel Trends; Intimacy;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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