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. Moreover, new published web services often have lower priorities to be selected for recommendations. 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 three algorithms (user-based, operation-based and priority-based) to validate our approach. We also provide evaluation methods which indicate that our approach produces high quality recommendations in case users have stable behavior