The tremendous growth in the amount of available web services (WS) 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, text based recommendations approaches depend mainly on user's perspective, languages and notations which easily decrease recommendation's efficiency. In this paper, we propose to take into account user's behaviors instead of text based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement two algorithms based on Vector Space Model and Latent Semantic Indexing to validate our approach. We also provide evaluation methods with different datasets in order to compare and assert the efficiency of our two algorithms