Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems
Journal article
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. https://doi.org/10.1016/j.jcss.2017.03.007
Authors | Luo, C., Zhang, B., Xiang, Y. and Qi, M. |
---|---|
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 |
Year | 2017 |
Journal | Journal of Computer and System Sciences |
Publisher | Elsevier |
ISSN | 0022-0000 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.jcss.2017.03.007 |
Publication dates | |
27 Apr 2017 | |
Publication process dates | |
Deposited | 16 Jan 2018 |
Accepted | 25 Mar 2017 |
Accepted author manuscript | |
Output status | Published |
https://repository.canterbury.ac.uk/item/887x8/gaussian-gamma-collaborative-filtering-a-hierarchical-bayesian-model-for-recommender-systems
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