Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems

Luo, C., Zhang, B., Xiang, Y. and Qi, M. (2017) Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems. Journal of Computer and System Sciences. ISSN 0022-0000.

Full text not available from this repository.

Abstract

The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real datasets

Item Type: Article
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Social and Applied Sciences > School of Law, Criminal Justice and Computing
Depositing User: Dr Man Qi
Date Deposited: 16 Jan 2018 16:13
Last Modified: 16 Jan 2018 16:13
URI: https://create.canterbury.ac.uk/id/eprint/16806

Actions (login required)

Update Item (CReaTE staff only) Update Item (CReaTE staff only)

Downloads

Downloads per month over past year

View more statistics

Share

Connect with us

Last edited: 29/06/2016 12:23:00