Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. When comparing different layouts through which recommendations are presented, presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a …
Category: Algorithmic bias
Bias characterization, assessment, and mitigation in location-based recommender systems
Location-based recommender systems (LBRSs) provide suggestion for Points of Interest (POIs) in Location-based social networks. However, we can characterize different forms of bias, associated with polarized interactions of the users with the PoIs. Post-processing and hybrid mitigation approaches can help alleviate the impact of those biases. In a study, published in the Data Mining and …
Robust reputation independence in ranking systems for multiple sensitive attributes
Ranking systems that account for the reputation of the users can be biased towards different demographic groups, especially when considering multiple sensitive attributes (e.g., gender and age). Providing guarantees of reputation independence can lead to unbiased and effective rankings. Moreover, these rankings are also robust to attacks. In a study, published by the Machine Learning …
Regulating Group Exposure for Item Providers in Recommendation
Platform owners can seek to guarantee certain levels of exposure to providers (e.g., to bring equity or to push the sales of new providers). Rendering certain groups of providers with the target exposure, beyond-accuracy objectives experience significant gains with negligible impact in recommendation utility. In a SIGIR 2022 paper, with Mirko Marras, Guilherme Ramos, and …
Evaluating the Prediction Bias Induced by Label Imbalance in Multi-label Classification
Prediction bias is a well-known problem in classification algorithms, which tend to be skewed towards more represented classes. This phenomenon is even more remarkable in multi-label scenarios, where the number of underrepresented classes is usually larger. In light of this, we present a novel measure that aims to assess the bias induced by label imbalance …
Reputation Equity in Ranking Systems
Reputation-based ranking systems can be biased towards the sensitive attributes of the users, meaning that certain demographic groups have systematically lower reputation scores. Nevertheless, if we unbias the reputation scores considering one sensitive attribute, bias still occurs when considering different sensitive attributes. For this reason, reputation scores should be unbiased independently of any sensitive attribute …
Disparate Impact in Item Recommendation: a Case of Geographic Imbalance
Data imbalances, related to the country of production of an item, lead to the under-recommendation of items produced in the smaller (less represented) countries. Re-ranking the recommendation lists, by balancing item relevance with the promotion of items produced in smaller countries can introduce equity in terms of visibility and exposure, without affecting recommendation effectiveness. In …
From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems
The size of a provider’s catalog in a platform affects the exposure that will be given to that provider by session-based recommender systems. Small providers, that are as popular as the big ones, are likely to get under-exposed in the recommendations. In an ECIR 2021 paper, with Alejandro Ariza, Francesco Fabbri, and Maria Salamó, we highlight side effects …
Connecting user and item perspectives in popularity debiasing for collaborative recommendation
The probability of recommending an item and of this recommendation being successful are biased against item popularity. By minimizing the correlation between a positive user-item interaction and the item’s popularity, we can avoid popularity bias. The recommendation of less popular items can come without affecting recommendation effectiveness and with a positive effect on other beyond-accuracy …
Reputation (in)dependence in ranking systems: demographics influence over output disparities
Your reputation on the Web does not depend only on your behavior, but also on your sensitive attributes. Concretely, belonging to a minority demographic group affects your reputation and how your preferences are valued in online ranking systems. In a recent SIGIR 2020 paper with Guilherme Ramos, we considered reputation-based ranking systems, which is a …