Being able to assess explanation quality in recommender systems and by shaping recommendation lists that account for explanation quality allows us to produce more effective recommendations. These recommendations can also increase explanation quality according to the proposed properties, fairly across demographic groups. In a SIGIR 2022 paper, with Giacomo Balloccu, Gianni Fenu, and Mirko Marras, …
Author: borattoludovico
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. Nevertheless, only around half of the published studies are reproducible. When comparing the existing approaches under the same protocol, we get unexpected outcomes, such as the …
Enabling cross-continent provider fairness in educational recommender systems
The courses of teachers are under-recommended by state-of-the-art models, unless they belong to the country that offers more courses and attracts more ratings. Regulating how recommendations are distributed with respect to the country of provenience of the teachers enables equitable and effective recommendations (cross-continent provider fairness). In a paper published in the Future Generation Computing …
Provider fairness across continents in collaborative recommender systems
In the presence of data imbalances, where some demographic groups of providers are represented more than others, the items of all the demographic groups that are not the majority group are under-recommended. A mitigation that accounts for the representation of each demographic group allows to introduce equity in the recommendation process, without having an impact …
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 …
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 …