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The viewing set-based method has difficulties ensuring that a user will enjoy recommended programs, and the model-based collaborative filtering method contains system-side real-time recommendation problems because most recent ratings cannot be applied in the recommendations and it has increased calculating costs due to the training process. In this paper, we propose a personalized program recommender for smart TVs using memory-based collaborative filtering with a novel similarity method that is robust to cold-start conditions and faster than the often-used, existing similarity method. The proposed method can improve the recommendation performance of electronic program guides and recommender applications for smart TVs. We determined the prediction accuracy of the ratings under various conditions in order to evaluate the proposed method. As a result, we confirmed that the proposed method is effective for cold-start conditions.