Improving top-k recommendation via joint collaborative autoencoders

Abstract

In this paper, we propose a Joint Collaborative Autoencoder framework that learns both user-user and item-item correlations simultaneously, leading to a more robust model and improved top-K recommendation performance. More specifically, we show how to model these user-item correlations and demonstrate the importance of careful normalization to alleviate the influence of feedback heterogeneity. Further, we adopt a pairwise hinge-based objective function to maximize the top-K precision and recall directly for top-K recommenders. Finally, a mini-batch optimization algorithm is proposed to train the proposed model. Extensive experiments on three public datasets show the effectiveness of the proposed framework over state-of-the-art non-neural and neural alternatives.