Improving Top-K Recommendation via JointCollaborative Autoencoders Conference Paper uri icon


  • © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License. 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.

author list (cited authors)

  • Zhu, Z., Wang, J., & Caverlee, J.

citation count

  • 7

editor list (cited editors)

  • Liu, L., White, R. W., Mantrach, A., Silvestri, F., McAuley, J. J., Baeza-Yates, R. S., & Zia, L.

publication date

  • May 2019


  • ACM  Publisher