Fair Matrix Factorisation for Large-Scale Recommender Systems

09/09/2022
by   Riku Togashi, et al.
0

Modern recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. It is challenging to reconcile these requirements at a practical level. In this study, we argue that item fairness is particularly hard to optimise in a large-scale setting. The notion of item fairness requires controlling the opportunity of items (e.g. exposure) by considering the entire ranked lists for users. It hence breaks the independence of optimisation subproblems for users and items, which is the essential property for conventional scalable algorithms, such as implicit alternating least squares (iALS). This paper explores a collaborative filtering method for fairness-aware item recommendation, achieving computational efficiency comparable to iALS, the most efficient method for item recommendation.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/07/2021

A Graph-based Approach for Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems tha...
research
06/08/2023

Safe Collaborative Filtering

Excellent tail performance is crucial for modern machine learning tasks,...
research
04/02/2022

Optimizing generalized Gini indices for fairness in rankings

There is growing interest in designing recommender systems that aim at b...
research
04/15/2023

Intent-aware Ranking Ensemble for Personalized Recommendation

Ranking ensemble is a critical component in real recommender systems. Wh...
research
08/19/2019

Recommender Systems Fairness Evaluation via Generalized Cross Entropy

Fairness in recommender systems has been considered with respect to sens...
research
02/18/2021

Learning Fair Representations for Bipartite Graph based Recommendation

As a key application of artificial intelligence, recommender systems are...
research
07/15/2018

Magnitude Bounded Matrix Factorisation for Recommender Systems

Low rank matrix factorisation is often used in recommender systems as a ...

Please sign up or login with your details

Forgot password? Click here to reset