Pierre Hurlin

dblp:428/2333 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Recommender systems · 100%
Human-computer interaction and pervasive computing
1 paper
Design research and methods · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems
group recommendation
1.012026
Critical reflections on user studies' evaluation methods for group recommender systems · Int. J. Hum. Comput. Stud. 2026
Design research and methods › research methodology
user study methodology
1.012026
Critical reflections on user studies' evaluation methods for group recommender systems · Int. J. Hum. Comput. Stud. 2026

Methods — techniques the papers use, named apart from their topics

social choice · 2.0randomized controlled trials · 1.0randomized controlled trial · 1.0
YearPublicationVenuePosition
2026 Critical reflections on user studies' evaluation methods for group recommender systems
abstract
Social choice-based aggregation strategies are often used in group recommender systems to aggregate individual preferences or recommendations. However, previous works evaluating group recommenders with user studies found that the diversity of the group members’ preferences impacts the effectiveness of the strategies. In this paper, we highlight and address the methodological limitations of those previous works. Specifically, the methodologies we introduce demonstrate the following three novelties: 1) We evaluated the strategies from the viewpoint of an “internal evaluator”; 2) We introduced a novel methodology for modeling a fictional but realistic group with specific preference profiles for the group members, defining scenarios with concrete users and items, that are still mapped to specific group configurations; 3) We evaluated the understanding of the participants, by measuring how well they can successfully apply the aggregation strategy to a new scenario. To do this we performed a randomized controlled trial (n=444) using a mixed design with two between-subject factors (the used aggregation strategy and the presented explanation type ), and a within-subject factor (the group configuration ). Our results, with friend groups, showed significant differences in the effectiveness of the aggregation strategies depending on the specific group configuration (i.e., depending on the internal diversity of group members’ preferences), with noticeable differences between evaluations of what is good for the group – external evaluation – and what is good for the participant – internal evaluation . We conclude with methodological implications for group recommender systems.
Francesco Barile, Pierre Hurlin, Cedric Waterschoot, Nava Tintarev
Int. J. Hum. Comput. Stud.2