User models, which represent the needs of users an online information access system is meant to serve, can facilitate the design and evaluation of the system. However, these models typically rely on assumptions drawn from adult users, such as advanced literacy levels or the ability to evaluate retrieved content critically. Hence, they often overlook the distinct needs and search habits of children, despite the growing prevalence of information access systems among youngsters. In this work, we introduce a set of child anti-personas, user models representing classes of users for whom the system is not designed. To substantiate these mismatches, we investigate how primary school children's behaviors when interacting with different search tools diverge from the assumptions of typical user models. Thereafter, we identify recurring patterns of misalignment between children's behavior and system expectations and link these patterns to the risks identified by adult stakeholders involved in children's online searching. By representing children as anti-personas of contemporary search tools, this work contributes to the quest for more informed and less stereotypical user modeling when it comes to information access for underrepresented groups, such as children.

Agents based on Large Language Models (LLM) have introduced a new way of information seeking that could simplify the search process to suit children’s cognitive skills, as these agents often respond to natural language inquiries with easy-to-read and plausible answers. Still, with emotions playing a crucial role in children’s information seeking and consumption behaviours, it is important to consider whether these agents suit children’s emotional intelligence. With that in mind, in this work, we examine the emotional undertones of LLM agent responses for children’s inquiries. Considering the known impact of prompt engineering on an agent’s response, we investigate whether explicitly informing an agent that the user is a child influences the emotions conveyed in its response. Outcomes from this empirical study reveal the limitations of LLM agents to fit children’s emotional intelligence, with agents tending to over-amplify any underlying emotion in a child’s inquiry. With our findings, we advance knowledge in the role of emotions in children’s online search and offer insights that could be used to improve children’s online information access.

Information retrieval (IR) systems have predominantly been designed for and evaluated in mainstream user populations, creating a self-reinforcing cycle of exclusion for underserved groups, e.g. differently-abled individuals, cultural minorities, those facing economic constraints, experiencing digital divides, language barriers, or those at age extremes. This opinion paper presents findings from a collaborative brainstorming session on underrepresented users in IR, identifying key challenges and pathways toward more inclusive design. Our analysis reveals a critical loop: lack of access leads to underrepresentation in datasets, which in turn leads to inadequate consideration in system design, perpetuating exclusion. We present guidelines for breaking this cycle through four interconnected dimensions: interface design grounded in participatory methods and universal accessibility, adaptive algorithms that respond to diverse capabilities and contexts, evaluation methodologies that position underserved users as active collaborators rather than passive subjects, and responsible AI integration guided by user-centred principles.

Query performance prediction (QPP) methods have primarily been tailored to mainstream users, thus relying on the traditional concept of relevance. In the case of children, however, relevance goes beyond content-based resource-query matching, which is why we gauge the performance of existing QPP methods in estimating the fit of resources retrieved in response to child-formulated queries. Outcomes from our empirical exploration of various QPP methods using a traditional and a child-focused definition of relevance on 2 datasets reveal the limitations in the adaptability of existing methods to the context of child information retrieval.

Children regularly engage with online information access systems, yet much of our understanding of how they search has not been revisited in recent years. Further, the reasons children avoid certain search practices remain largely unexamined. To address these gaps, we introduce a tangible cardĝ€'based activity designed to elicit and make visible children's reasoning during search. Building on prior literature, we examine whether previously documented practices remain relevant for the current generation of young searchers. We, then, investigate children's explanations for avoiding particular practices, revealing insights into their understanding of the information-seeking process. Overall, this work contributes to a better understanding of children's decisionĝ€'making processes when conducting online searches in school contexts, shedding light on both rationally adopted and discarded online search practices in the classroom setting.

Recommender systems increasingly shape children's online experiences, yet the risks they might pose remain poorly understood. Formal risk assessments are rare, overlook children's experiences, treat them as a homogeneous group, and often consider risks on a population-wide level. To provide a more nuanced child-centered perspective on risks associated with recommenders, we conduct a comprehensive risk assessment that examines multiple risks simultaneously and captures individual-level exposure rather than solely aggregated metrics. We also explore behavioral characteristics that make some children more susceptible to risk exposure to grasp how individual differences shape the manifestation of risks. Our results highlight the need for risk assessments that recognize heterogeneity among children and understand when risks become systemic' through individualized recommendation patterns. These insights spark the discussion about child-aware recommenders and ongoing regulatory efforts for evidence-based accountability.

The concept of relevance in Information Retrieval (IR) has been extensively studied. However, most mainstream IR models have been developed with adult users in mind, assuming cognitive maturity and autonomous interaction. Younger searchers, who increasingly integrate IR systems into their information-seeking practices, differ in cognitive abilities, information needs, and limited digital knowledge, which shape how they judge relevance, often diverging from traditional definitions assumed to work for adults. This calls for a deeper understanding of how this underrepresented group judges online content. In this study, we explore how children interpret and determine relevance when searching for information online in primary school classrooms. As information-seeking in this context is often guided by teachers, we also probe their criteria for relevance. By comparing both perspectives, we uncover points of alignment and divergence. These findings contribute to revisiting the concept of relevance for the primary school context and, more broadly, to the design and evaluation of equitable, context-aware IR systems that support responsible and inclusive information seeking practices.

In 2011, Xavier Amatriain sounded the alarm: recommender systems research was “doing it all wrong” [1]. His critique, rooted in statistical misinterpretation and methodological shortcuts, remains as relevant today as it was then. But rather than correcting course, we added new layers of sophistication on top of the same broken foundations. This paper revisits Amatriain’s diagnosis and argues that many of the conceptual, epistemological, and infrastructural failures he identified still persist, in more subtle or systemic forms. Drawing on recent work in reproducibility, evaluation methodology, environmental impact, and participatory design, we showcase how the field’s accelerating complexity has outpaced its introspection. We highlight ongoing community-led initiatives that attempt to shift the paradigm, including workshops, evaluation frameworks, and calls for value-sensitive and participatory research. At the same time, we contend that meaningful change will require not only new metrics or better tooling, but a fundamental reframing of what recommender systems research is for, who it serves, and how knowledge is produced and validated. Our call is not just for technical reform, but for a recommender systems research agenda grounded in epistemic humility, human impact, and sustainable practice.

Simulation is widely used in recommender systems research to study algorithm behavior and its impact on users. A common strategy involves adopting a universal choice model to represent users, assuming all follow the same consumption patterns. This one-size-fits-all approach overlooks the diversity in user preferences and decision-making patterns. In this work, we scrutinize whether this universal view fails to account for unique user behavior, thus harming realism and reliability of simulation outcomes. We conduct multiple simulations with various recommendation algorithms and choice models in the movie domain, comparing outcomes to users’ organic consumption patterns. Further, we evaluate whether a holistic model that captures users’ differences in behavior would better reflect a wide user base. Our findings highlight the limitations of using a naive, universal choice model and emphasize the need for more nuanced, user-specific approaches to make contributions from simulation studies more reflective of real-world effects.

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 to their complexity, these systems cannot be evaluated strictly by the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the challenges of multistakeholder evaluation of recommender systems. We bring attention to the different aspects involved—from the range of stakeholders involved (including but not limited to providers and consumers) to the values and specific goals of each relevant stakeholder. We discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about incorporating these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.