28 citations · 113 across the 27 of their papers we have counts for
27 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…
Keeping an Eye on AI: A Framework for Effective Human Oversight of AI Systems
Susanne Gaube, Markus Langer, Tim Miller +17
The use of Artificial Intelligence (AI) in high-risk, decision-making scenarios presents technical, safety, and normative challenges; problems that may only be ameliorated by human…
A Little More Like This: Text-to-Image Retrieval with Vision-Language Models Using Relevance Feedback
Bulat Khaertdinov, Mirela Popa, Nava Tintarev
Large vision-language models (VLMs) enable intuitive visual search using natural language queries. However, improving their performance often requires fine-tuning and scaling to la…
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…
Patient-Centred Explainability in IVF Outcome Prediction
Adarsa Sivaprasad, Ehud Reiter, David McLernon +3
This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse fo…
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…
De-centering the (Traditional) User: Multistakeholder Evaluation of Recommender Systems
Robin Burke, Gediminas Adomavicius, Toine Bogers +7
Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due…
Does SpatioTemporal information benefit Two video summarization benchmarks?
Aashutosh Ganesh, Mirela Popa, Daan Odijk +1
An important aspect of summarizing videos is understanding the temporal context behind each part of the video to grasp what is and is not important. Video summarization models have…
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…
Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects
F. M. Cau, N. Tintarev
Opinionated users often seek information that aligns with their preexisting beliefs while dismissing contradictory evidence due to confirmation bias. This conduct hinders their abi…
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree
Adarsa Sivaprasad, Ehud Reiter, Nava Tintarev +1
In explainable artificial intelligence (XAI) research, the predominant focus has been on interpreting models for experts and practitioners. Model agnostic and local explanation app…
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,…
Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion
Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars +5
Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclus…
"It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online
Federico Bianchi, Stefanie Anja Hills, Patricia Rossini +3
Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. W…
Designing Explanations for Group Recommender Systems
A. Felfernig, N. Tintarev, T. N. T. Trang +1
Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want…
Disparate Impact Diminishes Consumer Trust Even for Advantaged Users
Tim Draws, Zoltán Szlávik, Benjamin Timmermans +3
Systems aiming to aid consumers in their decision-making (e.g., by implementing persuasive techniques) are more likely to be effective when consumers trust them. However, recent re…
Operationalizing Framing to Support Multiperspective Recommendations of Opinion Pieces
Mats Mulder, Oana Inel, Jasper Oosterman +1
Diversity in personalized news recommender systems is often defined as dissimilarity, and based on topic diversity (e.g., corona versus farmers strike). Diversity in news media, ho…
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
Tim Draws, Nava Tintarev, Ujwal Gadiraju +2
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewp…
Helping users discover perspectives: Enhancing opinion mining with joint topic models
Tim Draws, Jody Liu, Nava Tintarev
Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives. This paper explores how opinion…
Contextual Personalized Re-Ranking of Music Recommendations through Audio Features
Boning Gong, Mesut Kaya, Nava Tintarev
Users are able to access millions of songs through music streaming services like Spotify, Pandora, and Deezer. Access to such large catalogs, created a need for relevant song recom…