Personality traits influence how individuals engage, behave, and make decisions during the information-seeking process. However, few studies have linked personality to observable search behaviors. This study aims to characterize personality traits through a multimodal time-series model that integrates eye-tracking data and gaze missingness–periods when the user’s gaze is not captured. This approach is based on the idea that people often look away when they think, signaling disengagement or reflection. We conducted a user study with 25 participants, who used an interactive application on an iPad, allowing them to engage with digital artifacts from a museum. We rely on raw gaze data from an eye tracker, minimizing preprocessing so that behavioral patterns can be preserved without substantial data cleaning. From this perspective, we trained models to predict personality traits using gaze signals. Our results from a five-fold cross-validation study demonstrate strong predictive performance across all five dimensions: Neuroticism (Macro F1 = 77.69%), Conscientiousness (74.52%), Openness (77.52%), Agreeableness (73.09%), and Extraversion (76.69%). The ablation study examines whether the absence of gaze information affects the model performance, demonstrating that incorporating missingness improves multimodal time-series modeling. The full model, which integrates both time-series signals and missingness information, achieves 10-15% higher accuracy and macro F1 scores across all Big Five traits compared to the model without time-series signals and missingness. These findings provide evidence that personality can be inferred from search-related gaze behavior and demonstrate the value of incorporating missing gaze data into time-series multimodal modeling.

How people seek, request, and exchange information in social interactions is shaped by personality and situational context, connecting the fields of interactive information science and attribution theory in social psychology. In everyday life, people seek information to achieve goals, collaborate, and manage social conflicts. Understanding how individual traits and contextual factors influence information-seeking behavior remains a challenge. Recent advances with large language models (LLMs) enable the simulation of socially grounded information-seeking behaviors in realistic and controllable ways. We introduce CHARISMA, a simulation framework that uses LLMs to examine how personality traits and situational factors influence information seeking as a form of social behavior. CHARISMA leverages movie characters and public figures as personality anchors, drawing on LLMs’ knowledge to simulate human-like interaction. CHARISMA’s utility is demonstrated in two studies: (1) agreeable pairs resolve conflicts more successfully, and (2) low-agreeable agents compete for information, while high-agreeable agents cooperate through prosocial exchange.

Interactive information retrieval (IIR) systems, including search engines and conversational systems, are increasingly central to user experiences. However, rigorously evaluating their performance, particularly as interactions become highly personalized, remains a scientific challenge. While user simulation offers a powerful methodology for reproducible evaluation, its adoption is hindered by a steep learning curve and a fragmented landscape of complex tools. This half-day tutorial provides a practical, hands-on introduction to user simulation at varying levels of complexity, from foundational statistical models to advanced, LLM-driven frameworks. Through a series of guided problems, participants will acquire practical skills in using popular libraries, learning user models from data, and applying large language models (LLMs) to simulate user behavior. The tutorial concludes with evaluating the simulators themselves, providing participants with guidance on appropriate use cases and fidelity assessment.

This half-day interactive tutorial provides researchers with practical skills to use large language models (LLMs) for interactive information retrieval research.Through hands-on exercises and real-world research examples, participants will learn to set up LLMs locally, integrate them via APIs, and evaluate their outputs.The tutorial will explain which models suit specific research needs, offering participants a robust toolkit to enhance their work.Attendees will also gain insights into the latest developments in the field, ensuring they stay at the forefront of innovation.Ideal for researchers eager to explore new methodologies in information retrieval, this tutorial offers foundational knowledge and cutting-edge strategies to use LLMs in interactive information retrieval research.

Eye-tracking data has been shown to correlate with a user's knowledge level and query formulation behaviour. While previous work has focused primarily on eye gaze fixations for attention analysis, often requiring additional contextual information, our study investigates the memory-related cognitive dimension by relying solely on pupil dilation and gaze velocity to infer users' topic familiarity and query specificity without needing any contextual information. Using eye-tracking data collected via a lab user study N=18, we achieved a Macro F1 score of 71.25% for predicting topic familiarity with a Gradient Boosting classifier, and a Macro F1 score of 60.54% with a k-nearest neighbours (KNN) classifier for query specificity. Furthermore, we developed a novel annotation guideline - specifically tailored for question answering - to manually classify queries as Specific or Non-specific. This study demonstrates the feasibility of eye-tracking to better understand topic familiarity and query specificity in search.

Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized definitions, but often neglect the subjective nature of sexist language and fail to consider individual users' perspectives. To address this gap, we adopt a perspectivist approach, retaining diverse annotations rather than enforcing gold-standard labels or their aggregations, allowing models to account for personal or group-specific views of sexism. Using demographic data from Twitter, we employ large language models (LLMs) to personalize the identification of sexism.

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement. Contrary to the current understanding that offline evaluations fall short of supporting online evaluations, we indicate that when identifying the most engaging clarification questions from the user’s perspective, online and offline evaluations correspond with each other. We show that the query length does not influence the relationship between online and offline evaluations, and reducing uncertainty in online evaluation strengthens this relationship. We illustrate that an engaging clarification needs to excel from multiple perspectives, and SERP quality and characteristics of the clarification are equally important. We also investigate if human labels can enhance the performance of Large Language Models (LLMs) and Learning-to-Rank (LTR) models in identifying the most engaging clarification questions from the user’s perspective by incorporating offline evaluations as input features. Our results indicate that LTR models do not perform better than individual offline labels. However, GPT, an LLM, emerges as the standout performer, surpassing all LTR models and offline labels.

Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of modern user experiences is the integration of chatbots or voice assistants. The rapid evolution of Large Language Models (LLMs) has provided a powerful tool to build conversational applications. We present Walert, a customized LLM-based conversational agent able to answer frequently asked questions about computer science degrees and programs at RMIT University. Our demo aims to showcase how conversational information-seeking researchers can effectively communicate the benefits of using best practices to stakeholders interested in developing and deploying LLM-based chatbots. These practices are well-known in our community but often overlooked by practitioners who may not have access to this knowledge. The methodology and resources used in this demo serve as a bridge to facilitate knowledge transfer from experts, address industry professionals’ practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.

With the rise and growing prevalence of generative models, particularly multi-modal ones, it is an opportune time to explore beyond existing interactive information retrieval research trends. Indeed, it is essential to determine new avenues to explore how users interact with these models as well as revisit existing avenues that can be embellished with new technology. In this session, we aim to create a venue to workshop ideas that explore the future of search experiences and user interactions with information in a collaborative, low-pressure environment. This UnExplored FrontCHIIRs workshop enables participants to form a sub-community within CHIIR to facilitate further development of the proposed ideas and allow deeper collaborative problem-solving than just presenting late-breaking work.

Conversational assistants are becoming prevalent among the wider population due to their simplicity and increasing utility. However, the shortcomings of these tools are as renowned as their benefits. In this work, we present a "first look" at an extensive collection of conversational queries, aiming to identify limitations and improvement opportunities specifically related to information access (i.e., search interactions). We explore over 600,000 Google Assistant interactions from 173 unique users, examining usage trends and the resulting deficiencies and strengths of these assistants. We aim to provide a balanced assessment, highlighting the assistant's shortcomings in supporting users and delivering relevant information to user needs and areas where it demonstrates a reasonable response to user inputs. Our analysis shows that, although most users conduct information-seeking tasks, there is little evidence of complex information-seeking behaviour, with most interactions consisting of simple, imperative instructions. Finally, we find that conversational devices allow users to benefit from increased naturalistic interactions and the ability to apply acquired information in situ, a novel observation for conversational information seeking.

The increasing reliance on digital information necessitates advancements in conversational search systems, particularly in terms of information transparency. While prior research in conversational information-seeking has concentrated on improving retrieval techniques, the challenge remains in generating responses useful from a user perspective. This study explores different methods of explaining the responses, hypothesizing that transparency about the source of the information, system confidence, and limitations can enhance users' ability to objectively assess the response. By exploring transparency across explanation type, quality, and presentation mode, this research aims to bridge the gap between system-generated responses and responses verifiable by the user. We design a user study to answer questions concerning the impact of (1) the quality of explanations enhancing the response on its usefulness and (2) ways of presenting explanations to users. The analysis of the collected data reveals lower user ratings for noisy explanations, although these scores seem insensitive to the quality of the response. Inconclusive results on the explanations presentation format suggest that it may not be a critical factor in this setting.

Advancements in large language models (LLMs) have changed information retrieval, offering users a more personalised and natural search experience with technologies like OpenAI ChatGPT, Google Bard (Gemini), or Microsoft Copilot. Despite these advancements, research into user tasks and information needs remains scarce. This preliminary work analyses a Google Bard prompt log with 15,023 interactions called the Bard Intelligence and Dialogue Dataset (BIDD), providing an understanding akin to query log analyses. We show that Google Bard prompts are often verbose and structured, encapsulating a broader range of information needs and imperative (e.g., directive) tasks distinct from traditional search queries. We show that LLMs can support users in tasks beyond the three main types based on user intent: informational, navigational, and transactional. Our findings emphasise the versatile application of LLMs across content creation, LLM writing style preferences, and information extraction. We document diverse user interaction styles, showcasing the adaptability of users to LLM capabilities.

Research in conversational information seeking (CIS) is moving very rapidly in various directions such as user interaction, system design, and evaluation. The tutorial focuses on the theoretical foundations and information-seeking processes for CIS, as well as their evaluation. The tutorial aims to introduce and communicate CIS research to the community and discuss it from different perspectives, such as theoretical modelling, evaluation, and user simulation. Also, it aims at gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.

The COVID-19 pandemic has changed the way we work, study, and conduct research. Ongoing stresses and uncertainties of the pandemic have impacted research activities and collaborations, especially for graduate researchers1 and Early Career Researchers (ECRs)2. It has also changed the way we connect with the broader research communities. For example, in the last year, conferences were either postponed or held online. Even though many conferences implemented social activities, connecting online with peers is hard. Thus, serendipity and forming new bonds or research connections at conferences have been more complex. Indeed, graduate researchers and ECRs have increased challenges connecting and establishing new research connections in online driven environments. This workshops aims to empower graduate and ECRs, make new research connections, and foster a sense of belonging.

Conversational information seeking (CIS) involves interaction sequences between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. CIS recently attracted significant attention and advancements continue to be made. This tutorial follows the content of the recent Conversational Information Seeking book authored by several of the tutorial presenters. The tutorial aims to be an introduction to CIS for newcomers to CIS in addition to the recent advanced topics and state-of-the-art approaches for students and researchers with moderate knowledge of the topic. A significant part of the tutorial is dedicated to hands-on experiences based on toolkits developed by the presenters for conversational passage retrieval and multi-modal task-oriented dialogues. The outcomes of this tutorial include theoretical and practical knowledge, including a forum to meet researchers interested in CIS.

Asking clarification questions is an active area of research; however, resources for training and evaluating search clarification methods are not sufficient. To address this issue, we describe MIMICS-Duo, a new freely available dataset of 306 search queries with multiple clarifications (a total of 1,034 query-clarification pairs). MIMICS-Duo contains fine-grained annotations on clarification questions and their candidate answers and enhances the existing MIMICS datasets by enabling multi-dimensional evaluation of search clarification methods, including online and offline evaluation. We conduct extensive analysis to demonstrate the relationship between offline and online search clarification datasets and outline several research directions enabled by MIMICS-Duo. We believe that this resource will help researchers better understand clarification in search.

Undertaking a PhD is a challenging yet fulfilling experience. PhD candidates become deeply involved in developing a myriad of skills over many vital facets, including (but not limited to): (i) the development of their research ideas; (ii) learning how to conduct their research; (iii) engaging with others about their research - both locally and internationally; (iv) developing a profile as an independent researcher; and (v) developing their teaching portfolio. Of course, a candidate is likely to encounter many highs and lows during their candidature. Periods of turbulence can be overcome through the application of various techniques to adapt and learn from these experiences. This tutorial will partly aim to introduce attendees to several techniques to help them advance in the PhD process. It will be presented by two recent PhD graduates in the field of Interactive Information Retrieval (IIR), who are both close enough to their respective times as PhD students to remember the highs and lows of PhD life, yet be far enough removed from the process that they can adequately reflect and provide insights into their own experiences - both good and bad. This tutorial will empower attendees to share their own do's and don'ts, review their practices for success, and refine what productivity strategies work for them. It will provide an impartial platform for an open and honest discussion about the journey of undertaking a PhD, led by the presenters without judgement.

Recent research on conversational information seeking (CIS) mostly focuses on uni-modal interactions and information items. This per- spective paper highlights the importance of moving towards de- veloping and evaluating multi-modal conversational information seeking (MMCIS) systems as they enable us to leverage richer context, overcome errors, and increase accessibility. We bridge the gap between the multi-modal and CIS research and provide a formal definition for MMCIS. We discuss potential opportunities and research challenges in designing, implementing, and evaluating MMCIS systems. Based on this research, we propose and implement a practical open-source framework for facilitating MMCIS research.

Metrics of human behaviour and effort lie at the heart of improving information interaction and retrieval. However, while some measurements have become predominant, such as precision and recall, there are many elements of information interaction where either measures have yet to be created, accepted, or widely used. This workshop seeks to tease out these areas, finding novel measures, or novel uses of existing measures, to create better experimental tools to improve our understanding of information interaction, and help develop better systems to support it.

The third CAIR workshop brings together researchers and developers interested in advancing conversational systems in interactive information retrieval. The workshop builds on the first and second CAIR workshops held at SIGIR 2017 and 2018 and will focus on the continuing development of current challenges, user and system limitations, and evaluation of conversational systems for information retrieval. Participants will collaboratively explore different contexts (i.e., home, hospitals, or work settings), use cases, and interactivity forms (voice-only, multi-modal, screen-based) in which conversational search systems can be used. Possible outcomes include fostering novel and innovative methodologies (such as for data collection and evaluation), personalising conversational systems, and understanding ethical challenges---such as system transparency---from the user's perspective.

We describe a static, open-access news corpus using data from the Common Crawl Foundation, who provide free, publicly available web archives, including a continuous crawl of international news articles published in multiple languages. Our derived corpus, CC-News-En, contains 44 million English documents collected between September 2016 and March 2018. The collection is comparable in size with the number of documents typically found in a single shard of a large-scale, distributed search engine, and is four times larger than the news collections previously used in offline information retrieval experiments. To complement the corpus, 173 topics were curated using titles from Reddit threads, forming a temporally representative sampling of relevant news topics over the 583 day collection window. Information needs were then generated using automatic summarization tools to produce textual and audio representations, and used to elicit query variations from crowdworkers, with a total of 10,437 queries collected against the 173 topics. Of these, 10,089 include key-stroke level instrumentation that captures the timings of character insertions and deletions made by the workers while typing their queries. These new resources support a wide variety of experiments, including large-scale efficiency exercises and query auto-completion synthesis, with scope for future addition of relevance judgments to support offline effectiveness experiments and hence batch evaluation campaigns.

We introduce the novel research problem of task recognition in daily life. We recognize tasks such as project management, planning, meal-breaks, communication, documentation, and family care. We capture Cyber, Physical, and Social (CPS) activities of 17 participants over four weeks using device-based sensing, app activity logging, and an experience sampling methodology. Our cohort includes students, casual workers, and professionals, forming the first real-world context-rich task behaviour dataset. We model CPS activities across different task categories, results highlight the importance of considering the CPS feature sets in modelling, especially work-related tasks.

Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational search, it is important for conversational assistants to accurately detect and predict user intent in information-seeking conversations. In this paper, we investigate two aspects of user intent prediction in an information-seeking setting. First, we extract features based on the content, structural, and sentiment characteristics of a given utterance, and use classic machine learning methods to perform user intent prediction. We then conduct an in-depth feature importance analysis to identify key features in this prediction task. We find that structural features contribute most to the prediction performance. Given this finding, we construct neural classifiers to incorporate context information and achieve better performance without feature engineering. Our findings can provide insights into the important factors and effective methods of user intent prediction in information-seeking conversations.

Intelligent assistants can serve many purposes, including entertainment (e.g. playing music), home automation, and task management (e.g. timers, reminders). The role of these assistants is evolving to also support people engaged in work tasks, in workplaces and beyond. To design truly useful intelligent assistants for work, it is important to better understand the work tasks that people are performing. Based on a survey of 401 respondents' daily tasks and activities in a work setting, we present a classification of work-related tasks, and analyze their key characteristics, including the frequency of their self-reported tasks, the environment in which they undertake the tasks, and which, if any, electronic devices are used. We also investigate the cyber, physical, and social aspects of tasks. Finally, we reflect on how intelligent assistants could influence and help people in a work environment to complete their tasks, and synthesize our findings to provide insight on the future of intelligent assistants in support of amplifying personal productivity.

We conducted a laboratory-based observational study where pairs of people performed search tasks communicating verbally. Examination of the discourse allowed commonly used interactions to be identified for Spoken Conversational Search (SCS). We compared the interactions to existing models of search behaviour. We find that SCS is more complex and interactive than traditional search. This work enhances our understanding of different search behaviours and proposes research opportunities for an audio-only search system. Future work will focus on creating models of search behaviour for SCS and evaluating these against actual SCS systems.

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intent on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models.

We present preliminary findings from a study of mixed initiative conversational behaviour for informational search in an acoustic setting. The aim of the observational study is to reveal insights into how users would conduct searches over voice where a screen is absent but where users are able to converse interactively with the search system. We conducted alaboratory-based observational study of 13 pairs of participants each completing three search tasks with different cognitive complexity levels. The communication between the pairs was analyzed for interaction patterns used in the search process. This setup mimics the situation of a user interacting with a search system via a speech-only interface.

The increase of voice-based interaction has changed the way people seek information, making search more conversational. Development of effective conversational approaches to search requires better understanding of how people express information needs in dialogue. This paper describes the creation and examination of over 32K spoken utterances collected during 34 hours of collaborative search tasks. The contribution of this work is three-fold. First, we propose a model of conversational information needs (CINs) based on a synthesis of relevant theories in Information Seeking and Retrieval. Second, we show several behavioural patterns of CINs based on the proposed model. Third, we identify effective feature groups that may be useful for detecting CINs categories from conversations. This paper concludes with a discussion of how these findings can facilitate advance of conversational search applications.

We address the challenge of extracting query biased audio summaries from podcasts to support users in making relevance decisions in spoken document search via an audio‐only communication channel. We performed a crowdsourced experiment that demonstrates that transcripts of spoken documents created using Automated Speech Recognition (ASR), even with significant errors, are effective sources of document summaries or “snippets” for supporting users in making relevance judgments against a query. In particular, the results show that summaries generated from ASR transcripts are comparable, in utility and user‐judged preference, to spoken summaries generated from error‐free manual transcripts of the same collection. We also observed that content‐based audio summaries are at least as preferred as synthesized summaries obtained from manually curated metadata, such as title and description. We describe a methodology for constructing a new test collection, which we have made publicly available.

This research investigates a new interface paradigm for interactive information retrieval (IIR) which forces us to shift away from the classic "ten blue links" search engine results page. Instead we investigate how to present search results through a conversation over a speech-only communication channel where no screen is available. Accessing information via speech is becoming increasingly pervasive and is already important for people with a visual impairment. However, presenting search results over a speech-only communication channel is challenging due to cognitive limitations and the transient nature of audio. Studies have indicated that the implementation of speech recognizers and screen readers must be carefully designed and cannot simply be added to an existing system. Therefore the aim of this research is to develop a new interaction framework for effective and efficient IIR over a speech-only channel: a Spoken Conversational Search System (SCSS) which provides a conversational approach to defining user information needs, presenting results and enabling search reformulations. In order to contribute to a more efficient and effective search experience when using a SCSS, we intend for a tighter integration between document search and conversational processes.

No abstract available.

Presenting search results over a speech-only communication channel involves a number of challenges for users due to cognitive limitations and the serial nature of speech. We investigated the impact of search result summary length in speech-based web search, and compared our results to a text baseline. Based on crowdsourced workers, we found that users preferred longer, more informative summaries for text presentation. For audio, user preferences depended on the style of query. For single-facet queries, shortened audio summaries were preferred, additionally users were found to judge relevance with a similar accuracy compared to text-based summaries. For multi-facet queries, user preferences were not as clear, suggesting that more sophisticated techniques are required to handle such queries.