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.

本研究提出使用基于概率编程语言的混合公平意识推荐系统，以多个用户 - 用户和物品 - 物品相似性度量、内容和人口统计信息实现高效准确的推荐，同时解决了推荐偏见问题。与最先进的公平推荐系统相比，实验结果表明，我们的模型可以提供更准确、更公平的推荐。