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With the increase in E-commerce, Recommendation Systems are getting popular to provide recommendations of various items (movies, books, music) to users. To build the Recommendation System(RS), Collaborative Filtering (CF) techniques are proven efficient. The main two Collaborative Filtering techniques are User-Based and Item-Based, but from survey it can be said that item-based CF provides better recommendations. A novel approach, Ratio-Based CF provides recommendation depending upon the item has more accuracy amongst item-based CF technique. The problem with CF techniques is more execution time i.e. O(mn). To improve the execution time a parallel platform or technique can be adopted to reduce the time complexity of recommendation system. Hence, for better and faster recommendation parallel Ratio-Based Collaborative Filtering algorithm should be used