One-Class Recommendation System with PU-Learning

摘要

With the explosive growth of e-commerce recommendation systems are getting well-known Many kinds of recommendation systems are used in various websites The one-class problem in recommendation systems is also a kind of recommendation problems that we cannot ignore In the traditional one-class classification problem we usually come up with Positive and Unlabeled Learning (PU-Learning) We cannot apply PU-Learning directly because we only can get the user-item rating matrix In other words the information is not enough to be applied to PU-Learning The rating matrix does not have the available features Without getting the outlier information of the users and the items we have to define the features from the one-class rating matrix In this paper we proposed a PU-Learning framework which is focusing on the one-class recommendation problem called RPU (Recommendation by PU-Learning) We use MovieLens and TencentWeibo datasets to evaluate our methods RPU can get the best accuracy comparing with the baselines of one-class approaches In RPU we have a feature selection part We also contribute a few kinds of features that are appropriate for RPU The features not only perform great in our RPU framework but perform around 10% better by using One-Class SVM

獎項日期	2015 8月 27
原文	English
監督員	Hung-Yu Kao (Supervisor)