Social Web Mashups Full Completion via Frequent Sequence Mining

In this paper we address the problem of Web Mashups full completion which consists of predicting the most suitable set of (combined) services that successfully meet the goals of an end-user Mashup, given the current service (or composition of services) initially supplied. We model full completion as a frequent sequence mining problem and we show how existing algorithms can be applied in this context. To overcome some limitations of the frequent sequence mining algorithms, e.g., efficiency and recommendation granularity, we propose FESMA, a new and efficient algorithm for computing frequent sequences of services and recommending completions. FESMA also integrates a social dimension, extracted from the transformation of user-service interactions into user-user interactions, building an implicit graph that helps to better predict completions of services in a fashion tailored to individual users. Evaluations show that FESMA is more efficient outperforming the existing algorithms even with the consideration of the social dimension. Our proposal has been implemented in a prototype, SoCo, developed at Bell Labs.