A Fairness-aware Hybrid Recommender System

TL;DR

This paper introduces a hybrid fairness-aware recommender system that mitigates observation bias and data imbalance, improving recommendation fairness and accuracy using probabilistic soft logic.

Contribution

It presents a novel hybrid model combining multiple similarity measures and demographic data, addressing biases in recommender systems with probabilistic soft logic.

Findings

More accurate recommendations than existing fair systems

Reduced bias in recommendations

Effective handling of observation and data imbalance biases

Abstract

Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.