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.@en

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.

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An essential characteristic for unbiased and diligent information-seeking that can enable informed opinion formation and decision-making is intellectual humility (IH), the awareness of the limitations of one's knowledge and opinions. While researchers have recognized the potential to boost IH in individuals, the effect of such interventions on their search behavior, along with the broader significance of IH in the context of web search on debated topics remains unexplored. In this paper, we present the results of a preregistered user study (N = 299) that we conducted to (1) test the effect of three interventions that boost self-reported IH on opinionated individuals' search behavior and (2) explore the role of IH in the search process of opinionated individuals more broadly. IH-boosting interventions did not affect search behavior; we attribute this to the high familiarity of the search environment, prompting searchers to default to their usual search behavior. Still, explorations of the role of IH in the search process indicate that IH and IH-related search intentions should be considered as relevant factors in the pursuit of supporting unbiased and diligent search on debated topics. Based on our exploratory findings, we argue that future research should investigate interventions that are more directly integrated into the search process, as well as such that combine boosting IH with encouraging searchers to approach the search task in an IH-driven way and promoting transparency for appropriate reliance on the search system and ranking.

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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.

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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.

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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.

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In this work, we reason how focusing on Information Retrieval (IR) for children and involving them in participatory studies would benefit the IR community. The Child Computer Interaction (CCI) community has embraced the child as a protagonist as their main philosophy, regarding children as informants, co-designers, and evaluators, not just users. Leveraging prior literature, we posit that putting children in the centre of the IR world and giving them an active role could enable the IR community to break free from the preexisting bias derived from interpretations inferred from past use by adult users and the still dominant system-oriented approach. This shift would allow researchers to revisit complex foundational concepts that greatly influence the use of IR tools as part of socio-technical systems in different domains. In doing so, IR practitioners could provide more inclusive, and supportive information access experiences to children and other understudied user groups alike in different contexts.

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The AltRecsys workshop, held in conjunction with the 18th edition of the ACM Conference on Recommender Systems (RecSys) in Bari, Italy, provides a platform for highlighting “alternative” work in recommender systems. Modeled after alt.chi and the CRAFT sessions at the FAccT conference, AltRecSys offers a space to discuss interesting, preliminary, offbeat, unexpected, and critical ideas in recommender systems that do not (yet) fit well into the kinds of publications and formats for the main conference or traditional workshops. This workshop is not a venue to showcase research advances. Instead, it is envisioned as a forum where researchers, (industry) practitioners, and other associated stakeholders can exchange ideas and together identify areas of study and new questions to expand the discussions and research agendas of the RecSys community in future years. The call for contributions and the workshop sessions are centered around the question “what are the vital questions, needs, or opportunities that the RecSys community is currently overlooking?”@en

In the current digital landscape, humans take center stage. This has caused a paradigm shift in the realm of intelligent technologies, prompting researchers and (industry) practitioners to reflect on the challenges and complexities involved in understanding the (potential) users of the technologies they develop. In this chapter, we provide an overview of human factors in user modeling, a core component of human-centered intelligent solutions. We discuss critical concepts, dimensions, and theories that inform the design of user models that are more attuned to human characteristics. Additionally, we emphasize the need for a comprehensive user model that simultaneously considers multiple factors to represent the intricacies of individuals’ interests and behaviors. Such a holistic model can, in turn, shape the design of intelligent solutions that are better able to adapt and cater to a wide range of user groups.@en

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.

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In this work, we discuss the findings emerging from co-design sessions between children ages 6 to 11 and adults, which were conducted to advance knowledge on how to best support children using well-known search tools for online information discovery. Specifically, we argue that by leveraging scaffolding, gamification techniques, and design choices via an application, it is possible to enhance children's habits related to query formulation. Outcomes from this preliminary exploration reveal that gameplay incentives (e.g. levels, points, and other incentives like customization) are needed and effective in motivating further interaction with the application, which in turn leads to further utilization of the scaffolding needed to positively impact query formulation.

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Current approaches in automatic readability assessment have found success with the use of large language models and transformer architectures. These techniques lead to accuracy improvement, but they do not offer the interpretability that is uniquely required by the audience most often employing readability assessment tools: teachers and educators. Recent work that employs more traditional machine learning methods has highlighted the linguistic importance of considering semantic and syntactic characteristics of text in readability assessment by utilizing handcrafted feature sets. Research in Education suggests that, in addition to semantics and syntax, phonetic and orthographic instruction are necessary for children to progress through the stages of reading and spelling development; children must first learn to decode the letters and symbols on a page to recognize words and phonemes and their connection to speech sounds. Here, we incorporate this word-level phonemic decoding process into readability assessment by crafting a phonetically-based feature set for grade-level classification for English. Our resulting feature set shows comparable performance to much larger, semantically- and syntactically-based feature sets, supporting the linguistic value of orthographic and phonetic considerations in readability assessment.

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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.

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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.@en

When using web search engines to conduct inquiries on debated topics, searchers' interactions with search results are commonly affected by a combination of searcher and system biases. While prior work has mainly investigated these biases in isolation, there is a lack of a comprehensive understanding of web search on debated topics. Addressing this gap, we conducted an exploratory user study (N = 255), aimed at advancing the understanding of the intricate searcher-system interplay. Particularly, we investigated the relations between (i) search system exposure, searchers' attitude strength, prior knowledge, and receptiveness to opposing views, (ii) search interactions, and (iii) post-search epistemic states. We observed that search interaction was shaped by search system exposure, attitude strength, and prior knowledge, and that attitude change was influenced by the level of confirmation bias and initial attitude strength, but not search system exposure. Insights from this work suggest the need to adapt interventions that mitigate the risks of searcher and system bias to searchers' nuanced pre-search epistemic states. They further emphasize the threat of customizing the search ranking to enhance user satisfaction in the context of debated topics to responsible opinion formation.

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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.@en

The influence of ChatGPT and similar models on education is being increasingly discussed. With the current level of enthusiasm among users, ChatGPT is envisioned as having great potential. As generative models are unpredictable in terms of producing biased, harmful, and unsafe content, we argue that they should be comprehensively tested for more vulnerable groups, such as children, to understand what role they can play and what training and supervision are necessary. Here, we present the results of a preliminary exploration aiming to understand whether ChatGPT can adapt to support children in completing information discovery tasks in the education context. We analyze ChatGPT responses to search prompts related to the 4th grade classroom curriculum using a variety of lenses (e.g., readability and language) to identify open challenges and limitations that must be addressed by interdisciplinary communities.

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This paper contributes to the discussion initiated in a recent SIGIR paper describing a gap in the information retrieval (IR) literature on query understanding–where they come from and whether they serve their purpose. Particularly the connection between query variability and search engines regarding consistent and equitable access to all users. We focus on a user group typically underserved: children. Using preliminary experiments (based on logs collected in the classroom context) and arguments grounded in children IR literature, we emphasize the importance of dedicating research efforts to interpreting queries formulated by children and the information needs they elicit. We also outline open problems and possible research directions to advance knowledge in this area, not just for children but also for other often-overlooked user groups and contexts.

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We often use search engines when seeking information for opinion-forming and decision-making on debated topics. However, searching for resources on debated topics to gain well-rounded knowledge is cognitively demanding, leaving us vulnerable to cognitive biases, such as confirmation bias. This can impede well-informed decision-making, and on a societal level, snowball to compel extremism and polarization. Most existing approaches to support better search apply nudges that directly modify user behavior. Such interventions bear the risk of harming user autonomy. Here, we discuss the shift we envision towards autonomy-preserving interventions that boost users' metacognitive skills, specifically their intellectual humility (IH)-the ability to recognize the fallibility of one's beliefs and the limits of one's knowledge. While simple interventions to boost IH have shown promise, the effect on users' search behavior has yet to be investigated. We present critical research questions, challenges, and an initial research plan to advance knowledge in this area.

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Researchers worldwide have explored the behavioral nuances that emerge from interactions of individuals afflicted by mental health disorders (MHD) with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to a persuasive technology that is part of their everyday lives: web search engines (SE). Each day, users with MHD embark on information seeking journeys using popular SE, like Google or Bing. Every step of the search process for better or worse has the potential to influence a searcher's mindset. In this work, we empirically investigate what subliminal stimulus SE present to these vulnerable individuals during their searches. For this, we use synthetic queries to produce associated query suggestions and search engine results pages. Then we infer the subliminal stimulus present in text from SE, i.e., query suggestions, snippets, and web resources. Findings from our empirical analysis reveal that the subliminal stimulus displayed by SE at different stages of the information seeking process differ between MHD searchers and our control group composed of "average"SE users. Outcomes from this work showcase open problems related to query suggestions, search engine result pages, and ranking that the information retrieval community needs to address so that SE can better support individuals with MHD.

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