3 citations · 3 across the 6 of their papers we have counts for
8 papers
Post-Training Denoising of User Profiles with LLMs in Collaborative Filtering Recommendation
Ervin Dervishaj, Maria Maistro, Tuukka Ruotsalo +1
Implicit feedback -- the main data source for training Recommender Systems (RSs) -- is inherently noisy and has been shown to negatively affect recommendation effectiveness. Denois…
Measuring Individual User Fairness with User Similarity and Effectiveness Disparity
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
Individual user fairness is commonly understood as treating similar users similarly. In Recommender Systems (RSs), several evaluation measures exist for quantifying individual user…
The Quest for Reliable Metrics of Responsible AI
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +1
The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quant…
Stairway to Fairness: Connecting Group and Individual Fairness
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +2
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relation…
As easy as PIE: understanding when pruning causes language models to disagree
Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo +1
Language Model (LM) pruning compresses the model by removing weights, nodes, or other parts of its architecture. Typically, pruning focuses on the resulting efficiency gains at the…
Joint Evaluation of Fairness and Relevance in Recommender Systems with Pareto Frontier
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Fairness and relevance are two important aspects of recommender systems (RSs). Typically, they are evaluated either (i) separately by individual measures of fairness and relevance,…