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The ACM SIGIR Conference on Research and Development in Information Retrieval has been experiencing significant growth over the past few years. In 2023, SIGIR received a total of 822 full paper submissions and 1,787 papers/proposals. This has brought several challenges to the conference program organization. This article reports some of the main aspects of the work conducted by the program committee (PC) co-chairs for SIGIR 2023, and discusses emergent issues of the review process and program design. We expect that this document serve as a reference for future PC co-chars and as a starting point for further discussions towards improving the review process and overall conference experience. Date: 23--27 July 2023. Website: https://sigir.org/sigir2023/. [ABSTRACT FROM AUTHOR]

The 46th European Conference on Information Retrieval (ECIR 2024) was held in Glasgow, Scotland, during 24th-28th March 2024. The conference brought together over four hundred researchers from the UK, Europe and abroad. ECIR 2024 was a fully in-person conference with a total of 417 attendees, the largest number of in-person attendees of any ECIR. The conference received over 700 submissions, not including submissions to the workshops (280 Full Paper and 184 Short paper submissions). ECIR 2024 introduced a number of novelties, including a new Findings track, an IR4Good track, a new innovation called the "Collab-athon" to foster collaborations within the community, and the Keith van Rijsbergen Award to recognise researchers who have made significant contributions in using theory to advance the field of information retrieval. This report details the conference programme and events. Date: 24--28 March 2024. Website: https://www.ecir2024.org. [ABSTRACT FROM AUTHOR]

This paper reports on the 45th European Conference on Information Retrieval (ECIR 2023), held in Dublin, Ireland, during April 2--6, 2023. The conference was the largest ECIR ever, and brought together hundreds of researchers from Europe and abroad. For those who like numbers: First, we received 489 submissions in total (including 228 full and 153 short papers) excluding further workshop submissions. Second, the technical program committee consisted of 624 reviewers in total (including 27 chairs, 124 SPC, 473 reviewers) with many serving on multiple tracks. Third, the proceedings necessitated a third volume, and contains a total of 175 papers in 2,151 pages. The rest of this report details the conference program and events. Date: 2--6 April 2023. Website: https://ecir2023.org/. [ABSTRACT FROM AUTHOR]

The 9th ACM SIGIR / The 13th International Conference on the Theory of Information Retrieval (ICTIR 2023) held in Taiwan co-located with SIGIR 2023. This brief report provides an overview of ICTIR 2023 and introduces the student reviewer program, which provides an opportunity for the students to understand and contribute to the conference review process. Date: 23 July 2023. Website: https://sigir.org/ictir2023/. [ABSTRACT FROM AUTHOR]

The Seventh International Workshop on Narrative Extraction from Texts (Text2Story'24) was held on March 24th, 2024, in conjunction with the 46th European Conference on Information Retrieval (ECIR 2024) in Glasgow, Scotland. Over the day, more than 50 attendees engaged in discussions and presentations focused on recent advancements in narrative representation, extraction, and generation. The workshop featured two invited keynote addresses, fourteen research paper presentations, and a poster session. The workshop proceedings are available online.1 Date: 24 March 2024. Website: https://text2story24.inesctec.pt/. [ABSTRACT FROM AUTHOR]

The First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) was held as a physical (in-person) event on March 24, 2024, in conjunction with the 46th European Conference on Information Retrieval (ECIR 2024) in Glasgow (Scotland). The scientific program included paper, spotlight, and poster presentations. Two keynote talks were delivered by Francesco Fabbri (Spotify, Spain) and Ruihong Qiu (University of Queensland, Australia). This report presents an overview of the activities conducted during the workshop and the main topics covered. Date: 24 March 2024. Website: https://irongraphs.github.io/ecir2024/. [ABSTRACT FROM AUTHOR]

Greetings SIGIR Members! SIGIR in Taipei was a success. A big thank you to the general chairs Hsin-Hsi Chen, Wei-Jou (Edward) Duh, and Hen-Hsen Huang, for putting on a great conference, in a beautiful venue. Thank you also to the Program Chairs Makoto Kato, Josiane Mothe and Barbara Poblete for the excellent technical programming! We would like to congratulate the Best Paper winners Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney, Niklas Deckers, Simon Reich, Janek Bevendorff, Benno Stein, Matthias Hagen, and Martin Potthast for their paper The Information Retrieval Experiment Platform. We would also like to congratulate Alireza Salemi, Juan Altmayer Pizzorno, and Hamed Zamani whose paper A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering won the Best Student Paper Award at SIGIR this year. Preparations for SIGIR 2024 in Washington DC are well underway, as preparations for SIGIR 2025 in Padua are beginning. [ABSTRACT FROM AUTHOR]

The first edition of the workshop on Generative Information Retrieval (Gen-IR 2023) took place in July 2023 in a hybrid fashion, co-located with the ACM SIGIR Conference 2023 in Taipei (SIGIR 2023). The aim was to bring information retrieval researchers together around the topic of generative AI that gathered attention in 2022 and 2023 with large language models and diffusion models. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers. Two main research outcomes are the proceedings of the workshop1 and the potential research directions discussed in this report. Date: 27 July 2023. Website: https://coda.io/@sigir/gen-ir. [ABSTRACT FROM AUTHOR]

This article presents an overview on task-guided scientific knowledge retrieval as a way for researchers to overcome the limitations of human cognitive capacity that in the age of explosive digital information creates a cognitive bottleneck. Topics include prototypes of task-guided scientific knowledge retrieval, as well as a look at novel representations, tools, and services and a review of systems that aid researchers in all aspects of scientific inquiry and discovery.

The CHIIR 2023 conference was conducted as a hybrid event in Austin, TX, USA, attracting over 110 attendees both physically and virtually. The main conference program spanned over three days and comprised of single-track presentations. Two workshops were organized on the day prior to the main conference, while the doctoral consortium and tutorial took place one day after the main event. This report has been prepared by the conference General Co-Chairs. Date: 19--23 March 2023. Website: https://sigir.org/chiir2023/. [ABSTRACT FROM AUTHOR]

The annual International Society for Music Information Retrieval (ISMIR) conference is the world's leading research forum on processing, searching, organizing and accessing music-related data. The 23rd International Society for Music Information Retrieval Conference (ISMIR 2022) was held from 04--08 December, 2022 at the National Science Seminar Complex, Indian Institute of Science, Bengaluru, India. ISMIR 2022 is the first ever ISMIR conference to take place in India. Due to the changing global landscape as a result of the COVID-19 pandemic, the 23rd ISMIR conference became the first hybrid ISMIR conference, with both in-person and remote participants. The scientific program of the single-track conference comprised two keynotes, 113 research papers, six tutorials, 13 music pieces and five special sessions. The conference also included a Women in MIR (WiMIR) plenary session, industry presentations, a late-breaking/demo session, five satellite events, and multiple social events that showcased the rich musical and cultural heritage of India. In this report, we present a summary of the event, highlighting some unique challenges we faced organizing the hybrid conference and reflecting on the event for future improvements. Date: 04--08 December 2022. Website: https://ismir2022.ismir.net. [ABSTRACT FROM AUTHOR]

We present here a short report on the French Information Retrieval Conference CORIA 2024, held in La Rochelle, France, on the 3rd and 4th of April 2024. We describe the objectives of the CORIA and provide an overview of the event, the submissions, and the keynotes. Date: 3--4 April 2024. Website: http://coria.asso-aria.org/2024/. [ABSTRACT FROM AUTHOR]

The first edition of the International Workshop on Implicit Author Characterization from Texts for Search and Retrieval (IACT'23) was held on July 27th, 2023, in conjunction with the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) in Taipei, Taiwan. To support both online and in-person participation, the workshop was held as a hybrid event. Online participation was allowed using the EventX platform. During the course of the day, about 15 attendees had the opportunity to follow up and discuss the recent advances in topics related to identifying and extracting implicit information about authors (e.g., human or AI) from texts and using it in IR tasks. The workshop program included two invited keynotes, four paper presentations, and the report of the ColiE competition which was held as a part of the workshop1. The proceedings of the workshop are available online at https://ceur-ws.org/Vol-3477/. Date: 27 July 2023. Website: https://en.sce.ac.il/news/iact23. [ABSTRACT FROM AUTHOR]

The 2023 ACM SIGIR Annual Business Meeting took place as a hybrid event during the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval in Taipei on July 26, 2023. The business meeting was led by the chair of the SIGIR Executive Committee (EC), Vanessa Murdock. [ABSTRACT FROM AUTHOR]

The Sixth International Workshop on Narrative Extraction from Texts (Text2Story'23) was held on April 2nd, 2023, in conjunction with the 45th European Conference on Information Retrieval (ECIR 2023) in Dublin, Ireland. Continuing the tradition of past years, the workshop was held as a hybrid event. Online participation was allowed using the Zoom platform. During the course of the day, more than 50 attendees had the opportunity to follow up and discuss the recent advances in topics related to representation, extraction, and generation of narratives. The workshop program included two invited keynotes and nineteen paper presentations. The proceedings of the workshop are available online 1. Date: 2 April 2023. Website: https://text2story23.inesctec.pt/. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges and complexities associated with citations in research papers. It highlights the importance of citations in validating information and guiding further research, but acknowledges the confusion and difficulties students face in understanding different citation styles and formats. It also discusses the role of citation generators in helping students create accurate citations and mentions several popular citation generator tools.

The Fifth International Workshop on Narrative Extraction from Texts (Text2Story'22) was held on the April 10th, 2022, in conjunction with the 44th European Conference on Information Retrieval (ECIR 2022) in Stavanger, Norway. Due to the COVID-19 restrictions that are still active in some countries, the workshop was held as an hybrid event, combining a "live" in-person and a "virtual" online participation. The online participation was allowed using the Zoom platform. During the course of the day, more than 50 attendees - about 40 in person and 10 in zoom - had the opportunity to follow-up and discuss the recent advances in topics related to representation, extraction, and generation of narratives. The workshop program included two invited keynotes and twelve paper presentations. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-3117/ Date: 10 April, 2022. Website: https://text2story22.inesctec.pt/. [ABSTRACT FROM AUTHOR]

The article offers information on a nation's economy is described in numbers, but academicians use words to explain those numbers. It discusses that Scholarly papers forecasting the state of the economy are more than equally matched with opinion pieces in the daily press that also attempt to forecast the state of the economy; and mentions that EconLit, the primary database covering the scholarly literature of economics, is produced by the American Economic Association (AEA).

The First Search Futures Workshop, in conjunction with the Fourty-sixth European Conference on Information Retrieval (ECIR) 2024, looked into the future of search to ask questions such as: • How can we harness the power of generative AI to enhance, improve and re-imagine Information Retrieval (IR)? • What are the principles and fundamental rights that the field of Information Retrieval should strive to uphold? • How can we build trustworthy IR systems in light of Large Language Models and their ability to generate content at super human speeds? • What new applications and affordances does generative AI offer and enable, and can we go back to the future, and do what we only dreamed of previously? The workshop started with seventeen lightning talks from a diverse set speakers. Instead of conventional paper presentations, the lightning talks provided a rapid and concise overview of ideas, allowing speakers to share critical points or novel concepts quickly. This format was designed to encourage discussion and introduce a wide range of topics within a short period, thereby maximising the exchange of ideas and ensuring that participants could gain insights into various future search areas without the deep dive typically required in longer presentations. This report, co-authored by the workshop's organisers and its participants, summarises the talks and discussions. This report aims to provide the broader IR community with the insights and ideas discussed and debated during the workshop - and to provide a platform for future discussion. Date: 24 March 2024. Website: https://searchfutures.github.io/. [ABSTRACT FROM AUTHOR]

As Information Retrieval (IR) researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The IR literature on the fundamental topic of retrieval and ranking, for instance, has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in Learning to Rank (LtR). As we move towards even more complex, deep learning models in a wide range of applications, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time- and space-efficiency; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment. As a step towards bringing together experts from industry and academia and creating a forum for a critical discussion and the promotion of efficiency in the era of Neural Information Retrieval (NIR), we held the ReNeuIR workshop on July 15th, 2022 as a hybrid event---in person in Madrid, Spain along with online attendees---in conjunction with ACM SIGIR 2022. Recognizing the importance of this topic, approximately 80 participants answered our call and attended the workshop over three sessions. The event included a total of two keynotes and eight paper presentations, and concluded with a lively discussion where participants helped identify gaps in existing research and brainstormed future research directions. We had consensus in recognizing that efficiency is not simply latency, that a holistic, concrete definition of efficiency is needed to guide researchers and reviewers alike, and that more research is necessary in the development of efficiency-centered evaluation metrics and standard benchmark datasets, platforms, and tools. Date: 15 July, 2022. Website: https://ReNeuIR.org. [ABSTRACT FROM AUTHOR]

The Joint Conference of the Information Retrieval Communities in Europe (CIRCLE 2020) is the second joint conference of the French, Italian, and Spanish information retrieval communities. The three days of conference gathered interesting studies and research work on a wide range of topics on information retrieval, such as topic and document modeling, web search, information retrieval in e-government, social media, recommender systems, information retrieval evaluation, indexing and annotation, user profiling and interaction, frameworks and systems, and semantic extraction. It was hosted by Université de Toulouse, France in a holiday resort at Samatan. Date: 4--7 July, 2022. Website: https://www.irit.fr/CIRCLE/. [ABSTRACT FROM AUTHOR]

The 2022 ACM SIG IR Annual Business Meeting took place as a hybrid event during the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval in Madrid, Spain on July 14, 2022. The business meeting was led by the chair of the SIG IR Executive Committee (EC), Vanessa Murdock. [ABSTRACT FROM AUTHOR]

Simulation is used as a low-cost and repeatable means of experimentation. As Information Retrieval (IR) researchers, we are no strangers to the idea of using simulation within our own field---such as the traditional means of IR system evaluation as manifested through the Cranfield paradigm. While simulation has been used in other areas of IR research (such as the study of user behaviours), we argue that the potential for using simulation has been recognised by relatively few IR researchers so far. To this end, the Sim4IR workshop was held online on July 15th, 2021 in conjunction with ACM SIGIR 2021. Building on past efforts, the goal of the workshop was to create a forum for researchers and practitioners to promote methodology and development of more widespread use of simulation for IR evaluation. Around 80 participants took part over two sessions. A total of two keynotes, three original paper presentations, and eight 'encore talks' were presented. The main conclusions from the resultant discussion were that simulation has the potential to offer solutions to the limitations of existing evaluation methodologies, but there is more research needed toward developing realistic user simulators; and the development and sharing of simulators, in the form of toolkits and online services, is critical for successful uptake. Date: 15 July, 2021. Website: https://sim4ir.org. [ABSTRACT FROM AUTHOR]

The Fourth International Workshop on Narrative Extraction from Texts (Text2Story'21) was held on the April 1st, 2021, in conjunction with the 43rd European Conference on Information Retrieval (ECIR 2021). Due to the Covid-19 outbreak, the workshop was held online on Zoom platform. During the course of the day, an average of more than 80 attendees had the opportunity to follow-up and discuss the recent advances in extraction and formal representation of narratives. The workshop consisted of two invited keynotes and ten paper presentations. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-2860/ Date: 1 April, 2021. Website: https://text2story21.inesctec.pt/. [ABSTRACT FROM AUTHOR]

E-commerce search and recommendation usually operate on structured data such as product catalogs and taxonomies. However, creating better search and recommendation systems often requires a large variety of unstructured data including customer reviews and articles on the web. Traditionally, the solution has always been converting unstructured data into structured data through information extraction, and conducting search over the structured data. However, this is a costly approach that often has low quality. In this paper, we envision a solution that does entirely the opposite. Instead of converting unstructured data (web pages, customer reviews, etc) to structured data, we instead convert structured data (product inventory, catalogs, taxonomies, etc) into textual data, which can be easily integrated into the text corpus that trains LLMs. Then, search and recommendation can be performed through a Q/A mechanism through an LLM instead of using traditional information retrieval methods over structured data. [ABSTRACT FROM AUTHOR]

Users interact with digital systems with some underlying tasks in their minds. In our research scope, a task can be either single or multi-staged. A single-staged task is associated with a single information need, whereas a multi-staged one is associated with more than one information needs. An example of a single-staged task is suggesting related papers to an author while they are writing a section of the research papers. An example of a multi-staged task is planning for a vacation, where the different underlying information needs could be 'places to visit', 'booking accommodation' etc. In the process of accomplishing their task objectives, a user often needs to interact with an information retrieval (IR) system to address one or more information needs. For instance, for writing a research paper on a chosen topic, a user needs to look for existing research work related to the topic. Traditional IR systems do not take into account a user's task intent while showing search results to the user. In our work, we propose a methodology towards developing next generation IR systems (i.e. proactive IR systems) which seek to anticipate the task intent of a user from their interactions with digital systems in order to proactively suggest potentially relevant information sources to assist them to complete their tasks. Specifically speaking, in this PhD, we proposed an embedding approach that captures the task semantics from the interactions of a user with a digital system (e.g. laptop, desktop, smartphone etc.). The proposed embedding approach is then applied for the downstream tasks of providing proactive suggestions in both single and multi staged scenarios. For the single-staged task, we propose a simulation setup to simulate a user's reading and writing interactions in a desktop environment. For the multi-staged task, we focus on web search sessions where a user can have multiple information needs corresponding to a search task. We also proposed a reproducible evaluation framework to compare between different proactive suggestion models. Awarded by: Dublin City University, Ireland on 10 August 2021. Supervised by: Gareth Jones. Available at: https://procheta.github.io/sprocheta/Thesis.pdf. [ABSTRACT FROM AUTHOR]

ACM SIGIR 2021 conference was organized as a fully online event, with more than 1,000 attendees from dozens of countries, a large technical program, and several activities and initiatives not seen at SIGIR before. This conference report provides some of the important details of how the organizers navigated through changing environments - political, social, and public health - moving the conference from NYC to Montreal and then to online. In addition to that decision process, the report also highlights several new and renewed initiatives around diversity, equity, and inclusion (DEI). Written by the conference General Co-Chairs and the DEI Chairs, this report is meant to inform future conference organizers as well as the SIGIR community at large. Date: 15--21 July, 2021. Website: http://sigir.org/sigir2021/. [ABSTRACT FROM AUTHOR]

This paper outlines a conceptual framework for understanding recent developments in information retrieval and natural language processing that attempts to integrate dense and sparse retrieval methods. I propose a representational approach that breaks the core text retrieval problem into a logical scoring model and a physical retrieval model. The scoring model is defined in terms of encoders, which map queries and documents into a representational space, and a comparison function that computes query-document scores. The physical retrieval model defines how a system produces the top-k scoring documents from an arbitrarily large corpus with respect to a query. The scoring model can be further analyzed along two dimensions: dense vs. sparse representations and supervised (learned) vs. unsupervised approaches. I show that many recently proposed retrieval methods, including multi-stage ranking designs, can be seen as different parameterizations in this framework, and that a unified view suggests a number of open research questions, providing a roadmap for future work. As a bonus, this conceptual framework establishes connections to sentence similarity tasks in natural language processing and information access "technologies" prior to the dawn of computing. [ABSTRACT FROM AUTHOR]

We present a systematic literature review of gamification in information retrieval, identifying 32 peer-reviewed articles spanning a period from 2012 to present day. Our study highlights that, with the exception of crowdsourcing relevance judgments, very few studies have investigated the application of gamification to search systems. Moreover, in articles that we would strictly define as being related to gamification (i.e. not games with a purpose or serious games), a majority did not present any real-world systems, user studies or empirical results. In numerous other fields, gamification has been used to successfully increase user engagement and productivity. We argue that the potential of gamification has been overlooked in information retrieval and, in particular, in interactive search settings. We conclude by discussing experimental obstacles and the potential benefits of evaluating gamification strategies in future interactive search systems. [ABSTRACT FROM AUTHOR]

This article presents a current state of search, where it is lacking, and how artificial intelligence (AI) assistant agents can improve it. The article details the role of tasks in search, defines and classifies complex search tasks, describes AI agents that can improve the process and explores adding them to search engines. In addition, challenges and opportunities are presented that can serve to redefine information access and use.

The article focuses on Elsevier's integration of artificial intelligence (AI) into Scopus, enhancing research navigation and analysis. Topics include the introduction of Scopus AI, which simplifies searches with natural language queries; provides topic summaries with references; and offers advanced features like concept mapping and expert identification to aid researchers in exploring and understanding complex subjects efficiently.

I discuss fairness in Information Retrieval (IR) through the eyes of Cooper and Robertson's probability ranking principle. I argue that unfair rankings may arise from blindly applying the principle without checking whether its preconditions are met. Following this argument, unfair rankings originate from the application of learning-to-rank approaches in cases where they should not be applied according to the probability ranking principle. I use two examples to show that fairer rankings may also be more relevant than rankings that are based on the probability ranking principle. [ABSTRACT FROM AUTHOR]

In real applications, the label information on many data is inaccurate, or a completely reliable label needs to be obtained at a high cost. The previous supervised hashing algorithms consider only the label information in the mapping process from Euclidean space to Hamming space when learning hash codes. However, there is no doubt that these algorithms are suboptimal in maintaining the relationships between high-dimensional data spaces. To overcome this problem, this paper advances a new weakly supervised discrete discriminant hashing (WDDH) to ensure a more effective representation of data and better retrieval of information. First, we consider the nearest neighbour relationship between samples, and new neighbourhood graphs are constructed to describe the geometric relationship between samples. Second, the algorithm embeds the learning of the hash function into the model and optimises the hash codes by a one-step iterative updating algorithm. Finally, it is compared with the existing classical unsupervised hashing algorithm and supervised hashing algorithm on different databases. The results and discussion of the experiments clearly show that the proposed WDDH algorithm in this paper is more robust for data representation in learning low-quality label data, coarse-grained label data and noisy data. [ABSTRACT FROM AUTHOR]

The 13th Italian Information Retrieval Workshop is the thirteenth edition of the annual conference of the Italian information retrieval and recommender systems communities. The two days of the conference gathered interesting studies and research work on a wide range of topics on information retrieval, recommender systems, and natural language processing, such as Search and Ranking, Recommendation, Content Analysis, and Classification, Artificial Intelligence, NLP, Semantics, and Dialog, Domain-Specific Applications, Human Factors and Interfaces, and Evaluation. It was hosted by the National Research Council (CNR) of Italy and the University of Pisa in a conference facility in Pisa, Italy. Date: 8--9 June 2023. Website: http://iir2023.isti.cnr.it/. [ABSTRACT FROM AUTHOR]

The discipline of information retrieval (IR) has a long history of examination of how best to measure performance. In particular, there is an extensive literature on the practice of assessing retrieval systems using batch experiments based on collections and relevance judgements. However, this literature has only rarely considered an underlying principle: that measured scores are inherently incomplete as a representation of human activity, that is, there is an innate gap between measured scores and the desired goal of human satisfaction. There are separate challenges such as poor experimental practices or the shortcomings of specific measures, but the issue considered here is more fundamental - straightforwardly, in batch experiments the human-machine gap cannot be closed. In other disciplines, the issue of the gap is well recognised and has been the subject of observations that provide valuable perspectives on the behaviour and effects of measures and the ways in which they can lead to unintended consequences, notably Goodhart's law and the Lucas critique. Here I describe these observations and argue that there is evidence that they apply to IR, thus showing that blind pursuit of performance gains based on optimisation of scores, and analysis based solely on aggregated measurements, can lead to misleading and unreliable outcomes. [ABSTRACT FROM AUTHOR]

The 44th European Conference on Information Retrieval (ECIR'22) was held in Stavanger, Norway. It represents a landmark, not only for being the northernmost ECIR ever, but also for being the first major IR conference in a hybrid format. This article reports on ECIR'22 from the organizers' perspective, with a particular emphasis on elements of the hybrid setup, with the aim to serve as a reference and guidance for future hybrid conferences. Date: 10--14 April, 2022. Website: https://ecir2022.org. [ABSTRACT FROM AUTHOR]

The First Early Career Researchers Roundtable for Information Access Research Workshop, in conjunction with the Seventh ACM Conference on Human Information Interaction and Retrieval (CHIIR) 2022, looked into the future of research, collaborations, and self-development to ask the following. Where are the opportunities for researchers in a (post-)pandemic environment, especially for Early Career Researchers (ECRs)? What do we need to do to get there? Which practical implementations can the broader CHIIR community support? The workshop started with an invited talk. Instead of conventional paper presentations, the attendees discussed the lessons learned from working in a pandemic. This report, co-authored by the workshop's organisers and its participants, summarises the discussion. This report aims to provide the broader CHIIR community with feedback on the workshop and foster ideas raised by ECRs to support ECRs. Two primary outcomes are (i) ECRs are often enthusiastic about taking on roles within a community, but formal validation and recognition are needed for their efforts and (ii) that the role of a conference needs to be reevaluated optimising the benefits of attending the event. Date: 14 March 2022. Website: https://sites.google.com/view/ecrs4ir/home. [ABSTRACT FROM AUTHOR]

A recent study assessed the reproducibility of meta-analyses on clinical psychological interventions published between 2000-2020. Out of a sample of 100 papers, 217 meta-analyses were selected. The study attempted to retrieve the original data and reproduce the main results of each meta-analysis. The original data was successfully retrieved for 67% of the meta-analyses, but only 5% of these cases had data files ready for reuse. Discrepancies were found in some of the meta-analyses, but they were mostly minor and did not significantly impact the conclusions. The study highlights the challenges of data availability and sharing practices in meta-analysis. [Extracted from the article]

This article discusses the challenges of retrieving accurate and relevant information from the vast pool of biomedical literature. It highlights the use of large language models (LLMs) and transformer models to enhance question-answering tasks. The paper focuses on prompt enhancement using retrieval-augmented architecture as a strategy to guide LLMs in generating more meaningful responses for biomedical question-answering tasks. The findings emphasize the importance of prompt enhancement methods that utilize explicit signals in user queries to improve LLM-generated responses in specialized domains such as biology and biomedicine. [Extracted from the article]

Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disambiguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical coverage. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic similarity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/meronyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe concepts. We present a novel technique for extracting ' is-a ' and ' part-whole ' relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia's semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks. [ABSTRACT FROM AUTHOR]

Worldwide, children in the care of the state constitute one of the most vulnerable groups in society. They are often not heard, or their views not respected in matters concerning them. This is incongruent with the Declaration of the Rights of the Child (1959) as well as the United Nations Convention on the Rights of the Child (UN, 1989). Guided by a conceptual framework of child participation theory, this article analyses South African legislation and policies to determine how and when child participation is being promoted. Findings indicate that providing information to children on how to participate meaningfully is the key for effective child participation and that practical guidelines should be developed. [ABSTRACT FROM AUTHOR]

Ranking is one of the central machine learning tasks arising from supervised learning and has many important applications in information retrieval. Many ranking approaches have been designed and extensively studied. In this paper we study two kernel ranking algorithms, the regularized least square ranking and its bias corrected version, which learn scoring functions from reproducing kernel Hilbert spaces (RKHSs). We say a kernel is perfect for ranking if an optimal scoring function lies in the associated RKHS. It is imperfect if an optimal scoring function can be approximated by the RKHS. The two regularized kernel ranking algorithms have been shown theoretically justified and empirically effective when the kernel is perfectly chosen. In practice, however, kernels are usually selected via cross validation process. It is more common than not that the kernel is imperfectly chosen. We develop a novel leave-two-out analysis technique to evaluate the generalization performance of the two regularized ranking algorithms in this situation. We show that they are still effective and can achieve the capacity independent optimal convergence rates. [ABSTRACT FROM AUTHOR]

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

We present a report on the Collab-a-thon, a series of sessions at the European Conference on Information Retrieval (ECIR) 2024 designed to help foster new collaborations during a conference. This report presents the motivation and design of the Collab-a-thon, a summary of the discussions covered at each session, and a set of recommendations for conducting similar events in the future. The event is set to run again at ECIR 2025 and planning is underway to pilot the event in a different community at the IEEE International Conference on Distributed Computing Systems (ICDCS) 2025. Date: 25--27 March 2024. Website: https://www.ecir2024.org/collab-a-thon/. [ABSTRACT FROM AUTHOR]

The 4th Workshop on Reducing Online Misinformation through Credible Information Retrieval (ROMCIR 2024) was part of the Satellite Events of the 46th European Conference on Information Retrieval (ECIR 2024). ROMCIR served as a platform for discussions on accessing accurate information and addressing the issue of information disorder prevalent in the online landscape at that time. In general, the challenge is multifaceted, encompassing various types of information sources (e.g., websites, social media posts,...) across different platforms and domains (e.g., fake news detection, health-related information retrieval, propaganda reduction). Additionally, in this edition, the critical need to assess the impact of generative models like Large Language Models (LLMs) on inadvertently amplifying misinformation and explore their potential role in supporting Information Retrieval Systems (IRSs) began to emerge. In this context, diverse approaches to the problem of access to truthful information found their place. Keynote speech and articles in this year's workshop mainly focused on themes such as health misinformation, multimedia and multimodal fact-checking, and information filtering to combat misinformation. Date: 24 March 2024. Website: https://romcir.disco.unimib.it/. [ABSTRACT FROM AUTHOR]

The 6th edition of the Search-Oriented Conversational AI workshop (SCAI 2021) was organised as a discussion platform on conversational AI for intelligent information access. The workshop was designed to be multidisciplinary, bringing together researchers and practitioners across the fields of natural language processing (NLP), information retrieval (IR), machine learning (ML) and human-computer interaction (HCI). The workshop included four sessions featuring invited talks, a separate poster session, and a session discussing the results of a shared task on conversational question answering (SCAI-QReCC). Date: 8 October, 2021. Website: https://scai.info. [ABSTRACT FROM AUTHOR]

Document clustering is a well-known text-mining method that assists in the categorization and comprehension of textual data. Document clustering is vital in areas like information retrieval, knowledge management, and marketing, underscoring the need for a highly accurate clustering model. Current models in document clustering face significant hurdles, such as dealing with sparse, high-dimensional representations based on the bag-of-words (BOW) approach, which are not only computationally demanding on large datasets but also lack in capturing the semantic nuances of documents. Additionally, these models struggle with determining the ideal number of clusters and managing datasets with overlapping elements. To overcome these issues, this paper introduces a novel co-clustering strategy that merges community detection methods from social network analysis with advanced text analysis techniques. The proposed method transforms documents into a network structure, where each document is a node and connections (edges) are formed between documents that are most similar. Community detection algorithms are then employed to identify clusters within this network of documents. The study explores various document representation methods, including topic modelling and sentence embedding, to provide a rich contextual understanding of the documents. An extensive evaluation is carried out, examining different combinations of community detection algorithms, clustering methodologies, and document representation strategies, particularly focusing on their efficacy in handling overlapping and non-overlapping datasets. The findings demonstrate that the Element-Centric evaluation measure is effective in enabling community detection algorithms to autonomously ascertain the most suitable number of clusters, yielding promising results for both overlapping and non-overlapping datasets. The LCD model shows remarkable performance in addressing overlapping datasets. Furthermore, the research reveals that innovative document representation approaches significantly enhance the performance of the models. Additionally, the use of topic modelling in conjunction with co-clustering algorithms proves effective in clearly depicting the themes within the clusters. • A co-clustering approach has been introduced for document clustering. • A network construction method is presented to transform documents into a network. • Community detection algorithms are used to extract clusters of connected documents. • Topic modelling and sentence embedding used for feature representation [ABSTRACT FROM AUTHOR]