11 citations · 26 across the 10 of their papers we have counts for
11 papers
Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation
Seungheon Doh, Sergio Oramas, Bruno Sguerra +3
The RecSys Challenge 2026 studies conversational music recommendation as a joint item recommendation and response generation problem: given a multi-turn dialogue, systems must retr…
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…
Bridging the Transparency Gap: Exploring Multi-Stakeholder Preferences for Targeted Advertisement Explanations
Dina Zilbershtein, Francesco Barile, Daan Odijk +1
Limited transparency in targeted advertising on online content delivery platforms can breed mistrust for both viewers (of the content and ads) and advertisers. This user study (n=8…
Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations
Roan Schellingerhout, Francesco Barile, Nava Tintarev
The increased use of information retrieval in recruitment, primarily through job recommender systems (JRSs), can have a large impact on job seekers, recruiters, and companies. As a…
Explaining Search Result Stances to Opinionated People
Z. Wu, T. Draws, F. Cau +3
People use web search engines to find information before forming opinions, which can lead to practical decisions with different levels of impact. The cognitive effort of search can…
A Co-design Study for Multi-Stakeholder Job Recommender System Explanations
Roan Schellingerhout, Francesco Barile, Nava Tintarev
Recent legislation proposals have significantly increased the demand for eXplainable Artificial Intelligence (XAI) in many businesses, especially in so-called `high-risk' domains,…
VideolandGPT: A User Study on a Conversational Recommender System
Mateo Gutierrez Granada, Dina Zilbershtein, Daan Odijk +1
This paper investigates how large language models (LLMs) can enhance recommender systems, with a specific focus on Conversational Recommender Systems that leverage user preferences…