Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems

by   Hao Wang, et al.

Recommender system is a widely adopted technology in a diversified class of product lines. Modern day recommender system approaches include matrix factorization, learning to rank and deep learning paradigms, etc. Unlike many other approaches, learning to rank builds recommendation results based on maximization of the probability of ranking orders. There are intrinsic issues related to recommender systems such as selection bias, exposure bias and popularity bias. In this paper, we propose a fair recommender system algorithm that uses Poisson process and Skellam distribution. We demonstrate in our experiments that our algorithm is competitive in accuracy metrics and far more superior than other modern algorithms in fairness metrics.


page 1

page 2

page 3

page 4


Pareto Pairwise Ranking for Fairness Enhancement of Recommender Systems

Learning to rank is an effective recommendation approach since its intro...

Curse of "Low" Dimensionality in Recommender Systems

Beyond accuracy, there are a variety of aspects to the quality of recomm...

Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction

Recommender system recommends interesting items to users based on users'...

KL-Mat : Fair Recommender System via Information Geometry

Recommender system has intrinsic problems such as sparsity and fairness....

Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

Recommender systems (RSs) employ user-item feedback, e.g., ratings, to m...

Fair Recommendation by Geometric Interpretation and Analysis of Matrix Factorization

Matrix factorization-based recommender system is in effect an angle pres...

A Recommender System-Inspired Cloud Data Filling Scheme for Satellite-based Coastal Observation

Filling missing data in cloud-covered areas of satellite imaging is an i...

Please sign up or login with your details

Forgot password? Click here to reset