A Hybrid Matrix Factorization Method with Isolation Forest for Recommendation System

Abstract

Matrix Factorization (MF), which is a traditional Collaborative Filtering (CF) technology, has been widely used in recommendation system. MF model relies on exiting user-item ratings, which maybe contains some noise because of intrusion attack, error of log system or mistake of artificial data. In order to detect these data noises and enhances the rating prediction accuracy, we propose a new method, a hybrid matrix factorization technique with Isolation Forest (IForest), which is shown to be highly effective in detecting anomalies with extremely high efficiency. IForest detects anomalies by builds an ensemble of iTrees for a given data set, then anomalies are those instances which have short average path lengths on the iTrees. Extensive experiment results on movieslens (1M) datasets show that our hybrid model outperforms other methods in effectively utilizing side information and achieves performance improvement.