Recurrent Recommendation with Local Coherence Conference Paper uri icon


  • © 2019 Association for Computing Machinery. We propose a new time-dependent predictive model of user-item ratings centered around local coherence - that is, while both users and items are constantly in flux, within a short-term sequence, the neighborhood of a particular user or item is likely to be coherent. Three unique characteristics of the framework are: (i) it incorporates both implicit and explicit feedbacks by extracting the local coherence hidden in the feedback sequences; (ii) it uses parallel recurrent neural networks to capture the evolution of users and items, resulting in a dual factor recommendation model; and (iii) it combines both coherence-enhanced consistent latent factors and dynamic latent factors to balance short-term changes with long-term trends for improved recommendation. Through experiments on Goodreads and Amazon, we find that the proposed model can outperform state-of-the-art models in predicting users' preferences.

author list (cited authors)

  • Wang, J., & Caverlee, J.

citation count

  • 7

editor list (cited editors)

  • Culpepper, J. S., Moffat, A., Bennett, P. N., & Lerman, K.

publication date

  • January 2019


  • ACM  Publisher