The proliferation of technologies based on web services in the past few years has driven the exponential growth of the number of services available to the user. Due to the large number of candidates, it is difficult for a user to select the service best suited to their needs. Thus it is of paramount importance to devise a strategy to recommend the appropriate service to a given user. Collaborative Filtering (CF) is a widely employed technique to filter relevant data in Web Service

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... s (WSRs). Although several CF based WSR techniques have been proposed over the past few years, their performance still requires significant improvement.In this paper, we propose a CF approach that leverages the location of the users for the filtering process. This ensures a greater measure of similarity between users to aid in making recommendations. Moreover, we also consider the history of the user and the web service to produce accurate recommendations. This is done by assigning weights to a candidate based on the user or service history to produce a similarity measure allowing the system to accurately determine the preferences of a user and make recommendations accordingly. The results of our proposed method are simulated through a set of comprehensive experiments performed on a real world Web Service dataset that is used to determine its performance.