Uji Akurasi Algoritma Bipolar Slope One dan BW-Mine pada Sistem Rekomendasi

Abstract

The recommendation system is widely applied to various e-commerce. There are some problems that can cause the recommendation system to fail. This problem is about the massive vacuum of rating data (sparsity) and cold start. Therefore, the right recommendation method is needed to improve accuracy, so that the user can find the item according to desire.To achieve this goal, bipolar slope one is used to predict the rating. Bipolar slope one is used to predict the rating of an item. In predicting an item's rating, an item pattern is needed. This item pattern can be represented in the Assosiation Rule that found in the BW-Mine algorithm.The test was carried out with MAE involving 50 users of 200 items. The test results using MAE, obtained that sparsity has an influence on the accuracy of rating prediction generated in the recommendation system