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.

In an existing study, the InsideOut Framework is used to produce and explore the emotional profiles of search engines (SE) in response to queries formulated by children aged 9 to 11 in the classroom context, revealing the emotional diversity of SE responses. Since then, there have been significant technological advances in emotion detection and information access. In this work, we conduct a comprehensive reproducibility study where we probe today's emotional profile of SE using both a lexicon-based and a language-model based approach tailored to the Italian language, thus addressing an acknowledged limitation of the original study. Additionally, considering the prevalence of agents based on Large Language Models (LLM) as information access systems among children, we extend the analysis to capture the emotional undertones of LLM responses and juxtapose them to those of SE. Our findings emphasize the importance of leveraging the appropriate emotion detection technique to produce and explore emotional profiles and lead us to reflect on the interplay of emotions on children's search-as-learning experience.

The rise of digital platforms for accessing online content-from popular search engines to social media sites- has contributed to the (un)intentional propagation of misleading information. This phenomenon, known as Information Disorder, affects individuals and society. Extensive research has been conducted to study and address Information Disorder as it pertains to the general population. Yet, little is known about how children, who have specific needs and behaviours when interacting with digital content, deal with misleading information and how the algorithms, that underlay the information access tools they use, mitigate or exacerbate the issue. Through a systematic literature review, we present research efforts that address or discuss the impact of Information Disorder on children and their overall information-seeking experience. We analyse the literature from various perspectives, including children's behaviour across platforms and the solutions developed to mitigate misleading information. Inspired by the knowledge distilled and gaps identified in our review, we discuss research directions that tackle both technological and human-centred challenges children face when dealing with misleading information, seeking to establish a foundation to mitigate the effects of Information Disorder among children.

The Workshop on Information Retrieval for Understudied Users (IR4U2) serves as a platform to highlight information retrieval (IR) research that directly impacts often understudied user groups. The second (IR4U2) workshop focuses on a user-centred AI perspective, which is vital for informing the design, development, and assessment of information retrieval systems that thoughtfully address the diverse needs of understudied populations, ensuring genuine accessibility and inclusivity. The objectives of IR4U2 are: (1) to build community and awareness by sharing AI and IR developments that serve underrepresented user groups in this research area; (2) to identify challenges and open issues along with lessons learned and challenges inherent to this area of research; and (3) to spark discussions that establish common frameworks for future research.

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.

There is a lack of a steady and solid influx of information retrieval (IR) research that has children (as the user group) as the protagonist. Existing work is scattered, conducted by only a few research groups, and often based on small-scale user studies or data that cannot be widely shared. Moreover, much of the current research focuses on specific age ranges and abilities, neglecting the broader spectrum of children's needs. Consequently, the paucity of IR research on how search and recommender systems serve and/or ultimately affect children translates into one of many 'Low-resource environments' in IR. Drawing from the literature and our experience in this area, we highlight key challenges and encourage greater attention from the IR community to address this critical gap.

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.

Recommendation algorithms are often trained using data sources reflecting the interactions of a broad user base. As a result, the dominant preferences of the majority may overshadow those of other groups with unique interests. This is something performance analyses of recommendation algorithms typically fail to capture, prompting us to investigate how well recommendations align with preferences of the overall population but also specifically a “non-mainstream” user group: children—an audience frequently exposed to recommender systems but rarely prioritized. Using music and movie datasets, we examine the differences in genre preferences between Children and Mainstream Users. We then explore the degree to which (genre) consumption patterns of a mainstream group impact the recommendations classical algorithms offer children. Our findings highlight prominent differences in consumption patterns between Children and Mainstream Users; they also reflect that children’s recommendations are impacted by the preference of user groups with deviating consumption habits. Surprisingly, despite being under-represented, children do not necessarily receive poorer recommendations. Further, our results demonstrate that tailoring training specifically to children does not always enhance personalization for them. These findings prompt reflections and discussion on how recommender systems can better meet the needs of understudied user groups.

Studying the interplay of children and recommender systems (RS) is ethically and practically challenging, making simulation a promising alternative for exploration. However, recent simulation approaches that aim to model natural user-RS interactions typically rely on behavioral data and assume that user preferences remain consistent over time—an assumption that may not hold for children who undergo continuous developmental changes. With that in mind, we explore the extent to which simulations based on historical data can meaningfully reflect children’s long-term consumption patterns. We do this via a simulation study using real-world data in which user behavior is modeled from observed listening preferences. Specifically, we probe whether simulation mirrors user preferences over time by comparing with organic (i.e., real) consumption patterns. Our findings offer a critical reflection on the reliability of simulation-based RS research for children and question the reliability of using behavioral assumptions to model users.

Recommender systems research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users. Recently, Ungruh et al. demonstrated that children’s consumption patterns and preferences differ from those of mainstream users, resulting in inconsistent recommendation algorithm performance and behavior for this user group. These findings, however, are based on two datasets with a limited child user sample. We reproduce and replicate this study on a wider range of datasets in the movie, music, and book domains, uncovering interaction patterns and aspects of child-recommender interactions consistent across domains, as well as those specific to some user samples in the data. We also extend insights from the original study with popularity bias metrics, given the interpretation of results from the original study. With this reproduction and extension, we uncover consumption patterns and differences between age groups stemming from intrinsic differences between children and others, and those unique to specific datasets or domains.

The way people seek, access, and use information for learning has changed. Once the primary gateway to information, search engines now share the stage with various digital/social platforms. This change is perhaps more notable among teenagers and has undoubtedly influenced how they browse and select resources to support their learning. To understand their habits and how alternatives to search engines have influenced them, in this work, we explore how high school students conduct online inquiries in the classroom. Our findings reveal that search engines are not always students’ first choice; social networks often play a leading role. This shift has important implications for the design of information retrieval technology, as researchers should consider how teenagers—an understudied population—use this range of tools. In addition, it is critical to foster search and media literacy skills among young users, who increasingly turn to tools not designed to search for information for educational purposes.

Information Retrieval (IR) systems have a wide range of impacts on consumers. We offer maps to help identify goals IR systems could—or should—strive for, and guide the process of scoping how to gauge a wide range of consumer-side impacts and the possible interventions needed to address these effects. Grounded in prior work on scoping algorithmic impact efforts, our goal is to promote and facilitate research that (1) is grounded in impacts on information consumers, contextualizing these impacts in the broader landscape of positive and negative consumer experience; (2) takes a broad view of the possible means of changing or improving that impact, including non-technical interventions; and (3) uses operationalizations and strategies that are well-matched to the technical, social, ethical, legal, and other dimensions of the specific problem in question.

With this paper, we delve into the problem of misinformation propagation in the video recommendation domain, focusing on top-N recommendation algorithms (RAs). We evaluate a broad spectrum of RAs to probe their ability to minimize misinformation recommendations while optimizing the RAs for overall performance. The results of an empirical exploration conducted using a suite of Top-N RAs and a video recommendation dataset [1] show that certain RAs excel in both performance and misinformation handling, while others struggle in mitigating misinformation. Our findings emphasize the potential of neighbourhood-based, neural, and other advanced collaborative filtering (CF) approaches in combating misinformation and contributing to more responsible recommender systems. Inspired by our findings, we propose investigating hybrid RAs and exploring specific features influencing misinformation recommendations, to further enhance the understanding and effectiveness of mitigating misinformation in recommendation systems.

Children often interact with search engines within a classroom context to complete assignments or discover new information. To successfully identify relevant resources among those presented on a search engine results page (SERP), users must first be able to comprehend the text included in SERP snippets. While this task may be straightforward for an adult user, children may encounter obstacles in terms of readability and comprehension when attempting to navigate a SERP. Previous research has demonstrated the positive impact of including visual cues on a SERP as relevance signals to guide children toward appropriate resources. In this work, we explore the effect of supplying visual cues related to readability and text difficulty on children's (ages 6-12) navigation of a SERP. Using quantitative data collected from user-interface interactions and qualitative data gathered from participant interviews, we analyze the impact of these visual cues on children's selection of results on a SERP when carrying out information discovery tasks.

Information Retrieval (IR) remains an active, fast-paced area of research. However, most advances in IR have predominantly benefited the so-called “classical” users, e.g., English-speaking adults. We envision IR4U2as a forum to spotlight efforts that, while sparse, consider diverse, and often understudied, user groups when designing, developing, assessing, and deploying the IR technologies that directly impact them. The key objectives for IR4U2 are: (1) raise awareness about ongoing efforts focused on IR technologies designed for and used by often understudied user groups, (2) identify challenges and open issues impacting this area of research, (3) ignite discussions to identify common frameworks for future research, and (4) enable cross-fertilization and community-building by sharing lessons learned from research catering to different audiences by researchers and (industry) practitioners across various disciplines.

Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.

Web search has evolved into a platform people rely on for opinion formation on debated topics. Yet, pursuing this search intent can carry serious consequences for individuals and society and involves a high risk of biases. We argue that web search can and should empower users to form opinions responsibly and that the information retrieval community is uniquely positioned to lead interdisciplinary efforts to this end. Building on digital humanism---a perspective focused on shaping technology to align with human values and needs---and through an extensive interdisciplinary literature review, we identify challenges and research opportunities that focus on the searcher, search engine, and their complex interplay. We outline a research agenda that provides a foundation for research efforts toward addressing these challenges.

We discuss the foundation of a collaborative effort to explore AI's role in supporting (teachers and) children in their learning experiences. We integrate principles of educational psychology, AI, and HCI, and align with best practices in education while undertaking a human-centered focus on design and development that puts the student at the centre and keeps the expert-in-the-loop. Initially, we study assessment items - questions or tasks tied to a learning target. These items vary in complexity, serve as indicators of students' grasp of specific concepts and spotlight areas where support may be needed. This preliminary analysis will help us outline a framework to guide the design and evaluation of AI technology for K-12 education. Such a framework would ensure that assessment item generation technology goes beyond the current one-dimensional approach by incorporating multifaceted, adaptable perspectives that consider the variegated landscape of learners' needs, subject matter complexities, and pedagogical goals.

Popularity bias is a prominent phenomenon in recommender systems (RS), especially in the music domain. Although popularity bias mitigation techniques are known to enhance the fairness of RS while maintaining their high performance, there is a lack of understanding regarding users’ actual perception of the suggested music. To address this gap, we conducted a user study (n=40) exploring user satisfaction and perception of personalized music recommendations generated by algorithms that explicitly mitigate popularity bias. Specifically, we investigate item-centered and user-centered bias mitigation techniques, aiming to ensure fairness for artists or users, respectively. Results show that neither mitigation technique harms the users’ satisfaction with the recommendation lists despite promoting underrepresented items. However, the item-centered mitigation technique impacts user perception; by promoting less popular items, it reduces users’ familiarity with the items. Lower familiarity evokes discovery—the feeling that the recommendations enrich the user’s taste. We demonstrate that this can ultimately lead to higher satisfaction, highlighting the potential of less-popular recommendations to improve the user experience.