Due to the exploding growth in the number of web services,mashup has emerged as a service composition technique to reuse existing services and create new applications with the least amount of effort.Service recommendation is essential to facilitate mashup developers locating desired component services among a large collection of candidates.However,the majority of existing methods utilize service profiles for content matching,not mashup descriptions.This makes them suffer from vocabulary gap and cold-start problem when recommending components for new mashups.In this paper,we propose a two-step approach to generate high-quality service representation from mashup descriptions.The first step employs a linear discriminant function to assign each term with a component service such that a coarse-grained service representation can be derived.In the second step,a novel probabilistic topic model is proposed to extract relevant terms from coarse-grained service representation.Finally,a score function is designed based on the final high-quality representation to determine recommendations.Experiments on a data set from ProgrammableWeb.com show that the proposed model significantly outperforms state-of-the-art methods.
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