Data Source Recommendation for Building Mashup Applications

Abstract

The emergence of mashup is gaining tremendous popularity and its application can be seen in a large number of domains. Along with the development of mashup technology, several mashup editors have been produced by the industry which can assist users to build mashups. However, with the increasing service and information sources distributed across the entire web space, even an easy to use mashup editor for nonprogrammers is not sufficient. In this paper, we apply the item based top-N recommendation algorithm which is widely used in e-Commerce area to recommend data source to the users while they are building mashups based on collected data of existing mashups. We also conduct experiment to evaluate the parameters of the recommendation algorithm and finally achieve very satisfactory results.