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 …
Category: Algorithmic bias
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 …