Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering (Supplementary Material)

This document contains experimental details and additional experimental results for the paper "Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering". In one run of the experiment, we randomly select 80% ratings for training and use the rest 20% for testing. The training data is further randomly split into four partitions, following the procedure of our proposed algorithm depicted in Fig.1. We then search them from {0.01, 0.05, 0.1, 0.5, 1} via cross-validation. We will show the experimental results later for different values of fold number.