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In this paper, we focus on Collaborative Filtering to provide recommendations to users that fit their profiles. We employed two methods: (1) K-Nearest Neighbors classifier, and (2) a fast implementation of Collaborative Filtering approach: “user-to-user fast XOR bit operation”. Both techniques serve the same objective, which is modifying the user's ontology profile (semantic profile). Technically, Collaborative Filtering extends the user's ontology profile based on the interests of a community of similar users. Also, we describe the implementation of the recommender system on a real platform, known as Hyper Many Media at Western Kentucky University. Finally, we evaluate the system based on Top-n-Recall and Top-n-Precision. The results show an improvement in Recall and Precision using Collaborative Filtering.