Non-personalized ranking systems that average the ratings of individual users are prone to attacks associated with bribing. Grouping users according to their preferences and weighting the average by the reputation of the users allows to generate more personalized rankings. These rankings are also less prone to attacks. In a paper published in the ACM Transactions …
The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems
The fact that most of the courses in MOOC platforms are offered by American teachers leads to the over-recommendation of these courses, at the expense of the courses produced in the other countries. A re-ranking that accounts for the country of production of a course, besides the relevance of the course for a user, is …
Interplay between Upsampling and Regularization for Provider Fairness in Recommender Systems
In the presence of a minority group of item providers in the data (characterized by a sensitive attribute, such as gender or age), the items of these providers are considered as of lower relevance and are recommended to the users with a lower visibility (i.e., fewer times) and a lower exposure (i.e., in lower positions …
What’s your Value of Travel Time? Collecting Traveler-centered Mobility Data via Crowdsourcing
Mobility solutions usually focus on time savings, proposing to users solutions that include the shortest or fastest paths. Nevertheless, users might perceive travel time as valuable (worthwhile) when it can be associated with other activities. In an ICWSM 2021 paper, with Cristian Consonni, Silvia Basile, Matteo Manca, André Freitas, Tatiana Kovacikova, Ghadir Pourhashem, and Yannick …
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 …
The effect of homophily on disparate visibility of minorities in people recommender systems
Demographics and homophily are the main drivers behind people recommendation in social networks and can affect the visibility that is given to users when they are recommended. These phenomena mainly impact users who belong to the minority groups, which have lower possibilities of being recommended, unless they are highly homophilic. In a recent ICWSM 2020 …