文章基本信息

标题：An Effective Model for Improving the Quality of Recommender Systems in Mobile E-Tourism
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作者：Masoumeh Mohammadnezhad ; Mehregan Mahdavi
期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2012
卷号：4
期号：1
页码：83
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In major e-commerce recommendation systems, the number of users and items is very large and availabledata are insufficient for identifying similar users. As a result, recommender systems could not use users'opinion to make suggestions to other users and the quality of the recommendations might reduce. Themain objective of our research is to provide high quality recommendations even when sufficient data areunavailable. In this article we have presented a model for this condition that combines recommendationmethods (e.g., Collaborative Filtering (CF) and Content Based Filtering (CBF)) with other methods suchas clustering and association rules. The model consists of four phases, at the first phase, tourists areclustered based on their location and the target tourist's cluster is sent to the next phase. In the secondphase, a two level graph is made based on the similarity between the tourist interests and the similarity ofthe tours. According to this graph, transitive relations are discovered among the tourists and k number ofitems that have the highest weight of relationships and are suggested to the target tourists. According tothe experiments, the standard F-measure indicates that the quality of the recommendations of this modelis higher than the traditional approaches which cannot discover transitive relationships.
关键词：Recommender systems; Collaborative filtering; Content based; Association rules and Graph theory