A recommender system based on historical usage data for web service discovery

The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user's perspective, languages, and notations, which easily decrease recommendation's efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.