A study on the improved collaborative filtering algorithm for recommender system

The purpose of this study is to suggest an algorithm of a recommender system to increase the customer's desire of purchasing, by automatically recommending goods transacted on e-commerce to customers. The recommender system has various filtering techniques according to the methods of recommendation. In this study, researchers study collaborative filtering among recommender systems. The accuracy of customer's preference prediction is compared with the accuracy of customer's preference prediction of the existing collaborative filtering algorithm, and the suggested new algorithm. At first, the accuracy of a customer's preference prediction of neighborhood based algorithm as automated collaborative filtering algorithm firstly & correspondence mean algorithm, is compared. It is analyzed by using MovieLens(1) 100K dataset and 1 Million dataset in order to experiment with the prediction accuracy of the each algorithm. For similarity weight used in both algorithms it is discovered Pearson's correlation coefficient and vector similarity which are generally used were utilized, and as a result of analysis, we show that the accuracy of the customer's preference prediction of correspondence mean algorithm is superior. Pearson's correlation coefficient and vector similarity used in two algorithms are calculated by using the preference rating of two customers' co-rated movies, and it shows that similarity weight is overestimated, where the number of co-rated movies is small. Therefore, it is intended to increase the accuracy of customer's preference prediction through expanding the effect of the number of the existing co-rated movies.