The tremendous growth in the amount of available web services impulses many researches 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 recommendations approaches depend mainly on user's perspective, languages and notations which easily decrease recommendation's efficiency. In this paper, we present our 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 three algorithms based on Vector Space Model to validate our approach. We also provide evaluation methods based on the precision, recall and root mean square error in order to compare and assert the efficiency of our algorithms