Name
Abstract | ||
|---|---|---|
A recommender system based on ranks is proposed, where an expertu0027s ranking of a set of objects and a useru0027s ranking of a subset of those objects are combined to make a prediction of the useru0027s ranking of all objects. The rankings are assumed to be induced by latent continuous variables corresponding to the grades assigned by the expert and the user to the objects. The dependence between the expert and user grades is modelled by a copula in some parametric family. Given a prior distribution on the copula parameter, the useru0027s complete ranking is predicted by the mode of the posterior predictive distribution of the useru0027s complete ranking conditional on the expertu0027s complete and the useru0027s incomplete rankings. Various Markov chain Monte-Carlo algorithms are proposed to approximate the predictive distribution or only its mode. The predictive distribution can be obtained exactly for the Farlie-Gumbel-Morgenstern copula family, providing a benchmark for the approximation accuracy of the algorithms. The method is applied to the MovieLens 100k dataset with a Gaussian copula modelling dependence between the expertu0027s and useru0027s grades. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Machine Learning | Bayesian inference,Ranking,Copula (linguistics),Parametric family,Copula (probability theory),MovieLens,Posterior predictive distribution,Artificial intelligence,Prior probability,Machine learning,Mathematics |
DocType | Volume | Citations |
Journal | abs/1802.03300 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Guillotte | 1 | 0 | 0.34 |
| François Perron | 2 | 20 | 2.37 |
| Johan Segers | 3 | 41 | 10.37 |