Recommendation systems provide customers with personalized recommendations by analyzing the purchase history. Item-based Collaborative Filtering (CF) algorithms recommend items which are similar to what the customers purchased before. Item-based algorithms are widely employed over user-based algorithms due to less computational complexity and better accuracy. We implement several types of item-based CF algorithms with P-Tree data structure. Similarity corrections and item effects are further included in the algorithms to exclude support and item variance. Our experiment on Netflix Prize data suggests support based similarity corrections and item effects have an significant impact on the prediction accuracy. Pearson and SVD item-feature similarity algorithms with support based similarity correction and item effects achieve better RMSE scores.