User Fairness in Recommender Systems

Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users

Recommender systems fairness evaluation via generalized cross entropy

Fairness in recommender systems has been considered with respect to sensitive attributes of users (e.g., gender, race) or items (e.g., revenue in a multistakeholder setting). Regardless, the concept has been commonly interpreted as some form of equality – i.e., the degree to which the system is meeting the information needs of all its users in an equal sense. In this paper, we argue that fairness in recommender systems does not necessarily imply equality, but instead it should consider a distribution of resources based on merits and needs.We present a probabilistic framework based ongeneralized cross entropy to evaluate fairness of recommender systems under this perspective, wherewe showthat the proposed framework is flexible and explanatory by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing. Results on two real-world datasets show the merits of the proposed evaluation framework both in terms of user and item fairnessThis work was supported in part by the Center for Intelligent Information Retrieval and in part by project TIN2016-80630-P (MINECO

Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can't enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.Comment: International Conference on Pattern Recognition and Method

Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems

Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popular items, thereby marginalizing less popular ones and compromising provider fairness. While previous research has recognized provider-side fairness issues, the investigation into how these biases affect beyond-accuracy aspects of recommendation systems - such as diversity, novelty, coverage, and serendipity - has been less emphasized. In this paper, we address this gap by introducing a simple yet effective post-processing re-ranking model that prioritizes provider fairness, while simultaneously maintaining user relevance and recommendation quality. We then conduct an in-depth evaluation of the model's impact on various aspects of recommendation quality across multiple datasets. Specifically, we apply the post-processing algorithm to four distinct recommendation models across four varied domain datasets, assessing the improvement in each metric, encompassing both accuracy and beyond-accuracy aspects. This comprehensive analysis allows us to gauge the effectiveness of our approach in mitigating provider biases. Our findings underscore the effectiveness of the adopted method in improving provider fairness and recommendation quality. They also provide valuable insights into the trade-offs involved in achieving fairness in recommender systems, contributing to a more nuanced understanding of this complex issue.Comment: FAccTRec at RecSys 202

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort