文章基本信息

标题：Providing A Model For Predicting Tour Sale In Mobile E-Tourism Recommender Systems
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作者：Masoumeh Mohammadnezhad ; Mehregan Mahdavi
期刊名称：International Journal of Information Technology Convergence and Services (IJITCS)
印刷版ISSN：2231-1939
电子版ISSN：2231-153X
出版年度：2012
卷号：2
期号：1
出版社：AIRCC
摘要：In this article, a new model is proposed for tourism recommender systems. This model recommends tours to tourists using data-mining techniques such as clustering and association rules. According to the proposed model, tourists are initially clustered. Self Organize Map (SOM) algorithm is used for determining the number of clusters and the clusters are created by K-means algorithm. Then, the clusters are analyzed and validated considering Quantization error, Topographic error and Davies-Bouldin error parameters. This model is implemented using two methods; according to the first method, recommendation is made based on tourists’ location, and in the second method this is done based on tourists’ behavioural patterns in the past. The results from evaluating the model using Pearson Correlation show that recommendations based on the behavioural patterns are closer to tourists’ interests.
关键词：Recommender systems; Clustering; Association rules mining; Cold start; Sparsity