TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Tourism Destination Recommender System for the Cold Start Problem |
| 영문제목(English Title) |
Tourism Destination Recommender System for the Cold Start Problem |
| 저자(Author) |
Xiaoyao Zheng
Yonglong Luo
Zhiyun Xu
Qingying Yu
Lin Lu
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| 원문수록처(Citation) |
VOL 10 NO. 07 PP. 3192 ~ 3212 (2016. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.
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| 키워드(Keyword) |
Recommender system
cold start
opinion mining
tourism destination
collaborative filtering
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| 파일첨부 |
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