19 citations · 28 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
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,…
Are Representation Disentanglement and Interpretability Linked in Recommendation Models? A Critical Review and Reproducibility Study
Ervin Dervishaj, Tuukka Ruotsalo, Maria Maistro +1
Unsupervised learning of disentangled representations has been closely tied to enhancing the representation intepretability of Recommender Systems (RSs). This has been achieved by…
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance
Theresia Veronika Rampisela, Tuukka Ruotsalo, Maria Maistro +1
Relevance and fairness are two major objectives of recommender systems (RSs). Recent work proposes measures of RS fairness that are either independent from relevance (fairness-only…