5 papers
Consensus vs. Dissent: Dynamic LLM Modeling of Subjective Preferences in Group Recommenders
Cedric Waterschoot, Nava Tintarev, Francesco Barile
Previous work in group recommender systems has demonstrated a sensitivity to the distribution of preferences within a group. Specifically, the selection of the preference aggregati…
Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco Barile
Large Language Models (LLMs) are increasingly being implemented as joint decision-makers and explanation generators for Group Recommender Systems (GRS). In this paper, we evaluate…
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations
Cedric Waterschoot, Nava Tintarev, Francesco Barile
Large Language Models (LLMs) are increasingly applied in recommender systems aimed at both individuals and groups. Previously, Group Recommender Systems (GRS) often used social cho…
With Friends Like These, Who Needs Explanations? Evaluating User Understanding of Group Recommendations
Cedric Waterschoot, Raciel Yera Toledo, Nava Tintarev +1
Group Recommender Systems (GRS) employing social choice-based aggregation strategies have previously been explored in terms of perceived consensus, fairness, and satisfaction. At t…
OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
Roan Schellingerhout, Francesco Barile, Nava Tintarev
The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairne…