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 …
Integrating Collaboration and Leadership in Conversational Group Recommender Systems
Interaction between the users, in a group setting, can support the decision-making when group recommendations have to be produced. Specifically, the presence of collaborative and leader users leads the group to trust these users when a final decision has to be taken. In a paper published in the ACM Transactions on Information Systems (TOIS), with …
Toward a Complete Data Valuation Process. Challenges of Personal Data
Data should be considered as a new asset, requiring new valuation rules, which do not apply to old commodities or to intangible assets (patents, intellectual property). In a paper published by the ACM Journal of Data and Information Quality, with Mihnea Tufiş, we discuss challenges related to the data valuation process, in connection to our …
A Robust Reputation-based Group Ranking System and its Resistance to Bribery
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 …