Collaborative filtering via learning characteristics of neighborhood based on convolutional neural networks

Abstract

Collaborative filtering (CF) is an extensively studied topic in Recommender System. Recent approaches use the statistical framework based on local Taylor approximations to unify both user based and item based CF algorithms and improve the performance of estimating unknown ratings. In this paper, we propose a new Machine Learning approach based on Convolutional Neural Networks to exploit complex latent user-item relations, using features extracted from the neighborhood of unknown rating via local approximations. Experimental results on two benchmark data sets demonstrate the effectiveness of the proposed approach via comparing to state-of-the-art methods.