An efficient and scalable ranking technique for mashups involving RSS data sources

Abstract

Mashups are key category of Web 2.0 personalized applications. Due to personalization property of Web 2.0 applications, number of mashups hosted by a mashup platform is increasing. End-users are overwhelmed by the increasing number of mashups. Therefore, they cannot easily find mashups of their interest. In this paper, we propose a novel mashup ranking technique based on the popular Vector Space Model (VSM) for mashups that use RSS feeds as data sources. Mashups that are ranked higher would be more interesting to end-users. In order to evaluate our mashup ranking technique, we implement it in a prototype where end-users select mashups that they consider interesting. We implicitly collect the end-user mashup selections and record the outcome of our ranking technique, and then we analyze them. Recorded R-Precision value in our technique is on an average 30% higher than R-Precision value in binary ranking technique which shows an improvement in capturing mashups that resemble end-user interest. In our design, we make sure our mashup ranking technique scales well to increasing number of mashups.