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http://dx.doi.org/10.3837/tiis.2016.07.018

Tourism Destination Recommender System for the Cold Start Problem  

Zheng, Xiaoyao (College of Territorial Resources and Tourism, Anhui Normal University)
Luo, Yonglong (School of Mathematics and Computer Science, Anhui Normal University)
Xu, Zhiyun (College of Territorial Resources and Tourism, Anhui Normal University)
Yu, Qingying (College of Territorial Resources and Tourism, Anhui Normal University)
Lu, Lin (College of Territorial Resources and Tourism, Anhui Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.7, 2016 , pp. 3192-3212 More about this Journal
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.
Keywords
Recommender system; cold start; opinion mining; tourism destination; collaborative filtering;
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