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Tourism Destination Recommender System for the Cold Start Problem
( Xiaoyao Zheng ),( Yonglong Luo ),( Zhiyun Xu ),( Qingying Yu ),( Lin Lu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.7
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.
A Method for Service Evaluation Based on Fuzzy Theory for Cloud Computing
( Liangmin Guo ),( Yonglong Luo ),( Xiaokang He ),( Guiyin Hu ),( Yan Dong ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.4
Aiming at the phenomenon of false information issued by service providers in cloud computing environment, a method for service evaluation based on fuzzy theory is put forward in this paper. According to the quality of services provided by cloud service providers and their behavior during interactions, a trust relationship between cloud service providers and cloud service consumers is established, which can be quantified by using fuzzy theory. The quality of services is evaluated by drawing on the trust relationship. In our method, the recommendation credibility of a cloud service consumer is determined through behavior similarity with evaluators and a praise factor. The introduction of the praise factor better suits the phenomenon of a high-quality service getting more repeat customers. The negative impact of dishonest customers is reduced, and the accuracy of trust and cloud service quality evaluation is improved by introducing a confidence factor that can be dynamically adjusted. The experimental results show that our method can effectively and accurately evaluate the trust value and service quality of providers, while weakening the influence of dishonest consumers, and quickly detect dishonest service providers. This is beneficial for consumers trying to find high quality service providers for similar services.