Recommended System for Neighborhoo-Based Collaborative Filtering Algorithm Using Pearson Correlation

Memory based collaborative filtering technique is successful approach to build a recommender system uses the known preferences of a group of users to make predictions of the unknown preferences for other users. In order to make such predictions the Pearson correlation coefficient is considered for user similarity. User-based Collaborative Filtering is efficient when compared to k-Nearest Neighbor algorithm (k-NN) and Item-based collaborative filtering algorithms from the experiment results. In this Paper a Memory based technique on user similarity using Pearson correlation coefficient is proposed and applied for Collaborative Filtering. The methodology using Pearson correlation coefficient used for predictions have been discussed. The Formulas that were used to implement these models including Pearson correlation coefficient, Weighted average rating, Simple weighted average and Prediction. The measured Mean Absolute Error (MAE) of the proposed model are compared with available models from literature and finally the performance analysis is done based on parameter MAE.