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- Title
Tourism Destination Recommender System for the Cold Start Problem.
- Authors
Xiaoyao Zheng; Yonglong Luo; Zhiyun Xu; Qingying Yu; Lin Lu
- 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.
- Subjects
TOURIST attractions; ELECTRONIC commerce; RECOMMENDER systems; SENTIMENT analysis; FILTERING software; COMPUTER network resources
- Publication
KSII Transactions on Internet & Information Systems, 2016, Vol 10, Issue 7, p3192
- ISSN
1976-7277
- Publication type
Article
- DOI
10.3837/tiis.2016.07.018