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3 </head> <body class="fixed-top-nav "> <header> <nav id="navbar" class="navbar navbar-light navbar-expand-sm fixed-top" role="navigation"> <div class="container"> <a class="navbar-brand title font-weight-lighter" href="/"> Sachin Pathiyan Cherumanal </a> <button class="navbar-toggler collapsed ml-auto" type="button" data-toggle="collapse" data-target="#navbarNav" aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"> <span class="sr-only">Toggle navigation</span> <span class="icon-bar top-bar"></span> <span class="icon-bar middle-bar"></span> <span class="icon-bar bottom-bar"></span> </button> <div class="collapse navbar-collapse text-right" id="navbarNav"> <ul class="navbar-nav ml-auto flex-nowrap"> <li class="nav-item "> <a class="nav-link" href="/">About Me </a> </li> <li class="nav-item active"> <a class="nav-link" href="/publications/">Publications <span class="sr-only">(current)</span> </a> </li> <li class="nav-item "> <a class="nav-link" href="/services/">Services </a> </li> <li class="nav-item "> <a class="nav-link" href="/activities/">
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3 <p><input type="text" id="bibsearch" spellcheck="false" autocomplete="off" class="search bibsearch-form-input" placeholder="Type to filter"></p> <div class="publications"> <h2 class="bibliography">2026</h2> <ol class="bibliography"><li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="ran2026rmitadmsmmuragneurips2025" class="col-sm-8"> <div class="title">RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition</div> <div class="author"> Kun Ran, Marwah Alaofi, Danula Hettiachchi, and <span class="more-authors" title="click to view 9 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '9 more authors' ? 'Chenglong Ma, Khoi Nguyen Dinh Anh, Khoi Vo Nguyen, Sachin Pathiyan Cherumanal, Lida Rashidi, Falk Scholer, Damiano Spina, Shuoqi Sun, Oleg Zendel' : '9 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">9 more authors</span> </div> <div class="periodical"> Feb 2026 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="/assets/pdf/ran2026rmitadmsmmuragneurips2025_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://github.com/rmit-ir/NeurIPS-MMU-RAG" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p>This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS 2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused retrieval-augmented generation (RAG) architecture composed of lightweight components that dynamically adapt the retrieval strategy based on inferred query complexity and evidence sufficiency. The system uses smaller LLMs, enabling operation on a single consumer-grade GPU while supporting complex research tasks. It builds on the G-RAG system, winner of the ACM SIGIR 2025 LiveRAG Challenge, and extends it with modules informed by qualitative review of outputs. R2RAG won the Best Dynamic Evaluation award in the Open Source category, demonstrating high effectiveness with careful design and efficient use of resources.</p> </div> </div> </div> </li></ol> <h2 class="bibliography">2025</h2> <ol class="bibliography"> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2025divfairtwosides" class="col-sm-8"> <div class="title">Diversification and Fairness in Search: Two Sides of the Same Coin?</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Falk Scholer, and Damiano Spina </div> <div class="periodical"> <em>ACM Transactions on Information Systems</em>, Oct 2025 </div> <div class="periodical"> Just Accepted </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3774320" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/pathiyancherumanal2025divfairtwosides.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://github.com/sachinpc1993/re-ranking-lightweight" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> </div> <div class="abstract hidden"> <p>Information retrieval systems aim to return relevant and useful content to users and are often biased towards popular items. This implies that an under-represented group or attribute will not receive a fair share of a userâs attention in search results. For example, while a ranked results list for a query such as âphysicistsâ might be fair according to a particular attribute such as gender, nationality, or social group, it might not be fair for all of them. Ideally, while providing relevant answers, a results list should also provide fair exposure across a broad range of attributes. We demonstrate that while a system can be fair towards multiple attributes, they are not necessarily diverse (i.e., redundancy/minimal novelty). To this end, we include an additional dimension to the study, i.e., diversity, and explore the relationship between fairness and diversity measures by exploring popular search result diversification techniques using the test collections from TREC 2021 Fair Ranking Track, TREC 2022 Fair Ranking Track and NTCIR-17 FairWeb-1. Furthermore, we study the impact of such diversification techniques along both nominal and ordinal attributes, as well as for intersectional fairness. Our results indicate that explicit search results diversification techniques showed improved results when the attributes were nominal but failed to provide fairer and more diverse results when the attributes were ordinal in nature. Additionally, in terms of intersectional fairness explicit search results diversification also performed significantly better than baseline retrieval runs.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="magnossaoangel2025effectsLLMPersonas" class="col-sm-8"> <div class="title">
3The Effects of Demographic Instructions on LLM Personas</div> <div class="author"> Angel Felipe Paula, J. Shane Culpepper, Alistair Moffat, and <span class="more-authors" title="click to view 3 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '3 more authors' ? 'Sachin Pathiyan Cherumanal, Falk Scholer, Johanne Trippas' : '3 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">3 more authors</span> </div> <div class="periodical"> <em>In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval</em>, Padua, Italy, Jul 2025 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3726302.3730255" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/magnossaoangel2025effectsLLMPersonas_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="/assets/pdf/magnossaoangel2025effectsLLMPersonas_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>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 usersâ 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. Our empirical results show that OpenAIâs LLMs (GPT-3.5, GPT-4, and GPT-4o) and two open-source LLMs (Mistral and Qwen) exhibit higher Krippendorffâs alpha label agreement with female annotators than with male annotators. As well, each LLM presents higher Krippendorffâs alpha agreement with a specific annotator age group. We then sought to counter these trends by providing âpersonaâ instructions as part of the LLM prompt, with somewhat surprising outcomes, highlighting the potential of user-centered perspectivist methods to improve content moderation systems.</p> </div> </div> </div> </li> </ol> <h2 class="bibliography">2024</h2> <ol class="bibliography"> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2024investigatingbiasinscs" class="col-sm-8"> <div class="title">Towards Investigating Biases in Spoken Conversational Search</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Falk Scholer, Johanne R Trippas, and <span class="more-authors" title="click to view 1 more author" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '1 more author' ? 'Damiano Spina' : '1 more author'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">1 more author</span> </div> <div class="periodical"> <em>In Companion Proceedings of the 26th International Conference on Multimodal Interaction</em>, San Jose, Costa Rica, Jul 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3686215.3690156" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/pathiyancherumanal2024investigatingbiasinscs_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="/assets/pdf/pathiyancherumanal2024investigatingbiasinscs_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>Voice-based systems like Amazon Alexa, Google Assistant, and Apple Siri, along with the growing popularity of OpenAIâs ChatGPT and Microsoftâs Copilot, serve diverse populations, including visually impaired and low-literacy communities. This reflects a shift in user expectations from traditional search to more interactive question-answering models. However, presenting information effectively in voice-only channels remains challenging due to their linear nature. This limitation can impact the presentation of complex queries involving controversial topics with multiple perspectives. Failing to present diverse viewpoints may perpetuate or introduce biases and affect user attitudes. Balancing information load and addressing biases is crucial in designing a fair and effective voice-based system. To address this, we (i) review how biases and user attitude changes have been studied in screen-based web search, (ii) address challenges in studying these changes in voice-based settings like SCS, (iii) outline research questions, and (iv) propose an experimental setup with variables, data, and instruments to explore biases in a voice-based setting like Spoken Conversational Search.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2024podcastmisinformation" class="col-sm-8"> <div class="title">
3Everything We Hear: Towards Tackling Misinformation in Podcasts</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Ujwal Gadiraju, and Damiano Spina </div> <div class="periodical"> <em>In Proceedings of the 26th International Conference on Multimodal Interaction</em>, San Jose, Costa Rica, Jul 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3678957.3678959" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/pathiyancherumanal2024podcastmisinformation_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://youtu.be/n0Ug8ZnjduE?si=HqoPRGOlnQu7RHAm&t=113" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="/assets/pdf/pathiyancherumanal2024podcastmisinformation_slides.pdf" class="btn btn-sm z-depth-0" role="button">Slides</a> </div> <div class="abstract hidden"> <p>Advances in generative AI, the proliferation of large multimodal models (LMMs), and democratized open access to these technologies have direct implications for the production and diffusion of misinformation. In this prequel, we address tackling misinformation in the unique and increasingly popular context of podcasts. The rise of podcasts as a popular medium for disseminating information across diverse topics necessitates a proactive strategy to combat the spread of misinformation. Inspired by the proven effectiveness of auditory alerts in contexts like collision alerts for drivers and error pings in mobile phones, our work envisions the application of auditory alerts as an effective tool to tackle misinformation in podcasts. We propose the integration of suitable auditory alerts to notify listeners of potential misinformation within the podcasts they are listening to, in real-time and without hampering listening experiences. We identify several opportunities and challenges in this path and aim to provoke novel conversations around instruments, methods, and measures to tackle misinformation in podcasts.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="ji2024towardsbiasscs" class="col-sm-8"> <div class="title">Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search</div> <div class="author"> Kaixin Ji, <em>Sachin Pathiyan Cherumanal</em>, Johanne R. Trippas, and <span class="more-authors" title="click to view 4 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '4 more authors' ? 'Danula Hettiachchi, Flora D. Salim, Falk Scholer, Damiano Spina' : '4 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">4 more authors</span> </div> <div class="periodical"> <em>In Adjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction</em>, Melbourne, VIC, Australia, Jul 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3640471.3680245" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/ji2024towardsbiasscs_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://drive.google.com/file/d/1yn_OZRtFfOU6cHM_ZOxXQA-EH06I4WCW/view?usp=share_link" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="/assets/pdf/ji2024towardsbiasscs_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>Spoken Conversational Search (SCS) poses unique challenges in understanding user-system interactions due to the absence of visual cues, and the complexity of less structured dialogue. Tackling the impacts of cognitive bias in todayâs information-rich online environment, especially when SCS becomes more prevalent, this paper integrates insights from information science, psych
3ology, cognitive science, and wearable sensor technology to explore potential opportunities and challenges in studying cognitive biases in SCS. It then outlines a framework for experimental designs with various experiment setups to multimodal instruments. It also analyzes data from an existing dataset as a preliminary example to demonstrate the potential of this framework and discuss its implications for future research. In the end, it discusses the challenges and ethical considerations associated with implementing this approach. This work aims to provoke new directions and discussion in the community and enhance understanding of cognitive biases in Spoken Conversational Search.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2024walert" class="col-sm-8"> <div class="title">Walert: Putting Conversational Information Seeking Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Lin Tian, Futoon M. Abushaqra, and <span class="more-authors" title="click to view 7 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '7 more authors' ? 'Angel Felipe Paula, Kaixin Ji, Halil Ali, Danula Hettiachchi, Johanne R. Trippas, Falk Scholer, Damiano Spina' : '7 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">7 more authors</span> </div> <div class="periodical"> <em>In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval</em>, Sheffield, United Kingdom, Jul 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3627508.3638309" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/pathiyancherumanal2024walert_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://drive.google.com/file/d/1QN8XVjBNKAZ06S1D6VkCcng1ej6giFE-/view?usp=share_link" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="/assets/pdf/pathiyancherumanal2024walert_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>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 professionalsâ practical needs, and foster a collaborative environment. The data and code of the demo are available at https://github.com/rmit-ir/walert.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="frummet2024ReportSCAI2024" class="col-sm-8"> <div class="title">Report on the 8th Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI 2024) at CHIIR 2024</div> <div class="author"> Alexander Frummet, Andrea Papenmeier, Maik Fröbe, and <span class="more-authors" title="click to view 15 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '15 more authors' ? 'Johannes Kiesel, Vaibhav Adlakha, Norbert Braunschweiler, Mateusz Dubiel, Satanu Ghosh, Marcel Gohsen, Christin Kreutz, Milad Momeni, Markus Nilles, Sachin Pathiyan Cherumanal, Abbas Pirmoradi, Paul Thomas, Johanne R. Trippas, Ines Zelch, Oleg Zendel' : '15 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">15 more authors</span> </div> <div class="periodical"> <em>In </em>, Aug 2024 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3687273.3687282" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/frummet2024ReportSCAI2024_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> </div> <div class="abstract hidden"> <p>Conversational Agents are increasingly integrated into our daily routines, assisting us with various tasks, from simple c
3ommands such as scheduling events to more complex conversational search interactions. Such conversational search systems are traditionally evaluated with word-overlap metrics such as F1 score and accuracy. The full-day workshop on Search-Oriented Conversational Artificial Intelligence (SCAI) at CHIIR 2024 explored the evaluation of conversational search systems from the userâs perspective. This interactive workshop included multiple panel discussions and working groups focused on developing and discussing innovative, user-centered evaluation methods for these systems. This paper, co-authored by both organizers and participants of the workshop, presents a summary of the insights gathered from the panel discussions and working groups.Date: 14 March 2024.Website: https://scai.info/scai-2024/.</p> </div> </div> </div> </li> </ol> <h2 class="bibliography">2023</h2> <ol class="bibliography"><li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2023rmit_ir" class="col-sm-8"> <div class="title">RMIT_IR at the NTCIR-17 FairWeb-1 Task</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Kaixin Ji, Danula Hettiachchi, and <span class="more-authors" title="click to view 3 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '3 more authors' ? 'Johanne R Trippas, Falk Scholer, Damiano Spina' : '3 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">3 more authors</span> </div> <div class="periodical"> <em>In The Seventeenth NTCIR Conference on Evaluation of Information Access Technologies Proceedings</em>, Aug 2023 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="/assets/pdf/pathiyancherumanal2023rmit_ir_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://youtu.be/VAJCDkbBDQk?si=mfoKheq_xyt2mT53" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Video</a> <a href="https://github.com/rmit-ir/fairweb-1" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="/assets/pdf/pathiyancherumanal2023rmit_ir_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>This report describes the participation of the RMIT IR group at the NTCIR-17 FairWeb-1 task. We submitted five runs with the aim of exploring the role of explicit search result diversification (SRD) and ranking fusion to generate fair rankings considering multiple fairness attributes. We also explored the use of a linear combination-based technique (LC) to take into consideration the relevance while re-ranking. In this report, we compared results from all our submitted runs against each other and the retrieval baselines along each topic type separately (i.e., Researcher, Movie, YouTube). Overall, our results show that neither the SRD-based runs nor the linear combination-based runs show any statistically significant improvement over the retrieval baselines.</p> </div> </div> </div> </li></ol> <h2 class="bibliography">2022</h2> <ol class="bibliography"> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2022rmit" class="col-sm-8"> <div class="title">RMIT CIDDA IR at the TREC 2022 Fair Ranking Track</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Marwah Alaofi, Reham Abdullah Altalhi, and <span class="more-authors" title="click to view 3 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '3 more authors' ? 'Elham Naghizade, Falk Scholer, Damiano Spina' : '3 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">3 more authors</span> </div> <div class="periodical"> <em>In The Thirty-First Text REtrieval Conference (TREC 2022) Proceedings</em>, Aug 2022 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="/assets/pdf/pathiyancherumanal2022rmit_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> </div> <div class="abstract hidden"> <p>This report describes the participation of the RMIT CIDDA IR group at the TREC 2022 Fair Ranking Track (Task 1). We submitted 8 runs with the aim to explore the role of explicit search result diversification, ranking fusion, and the use of a multi-criteria decision-making method to generate fair rankings considering multiple protected attributes.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2022dc" class="col-sm-8"> <div class="title">Fairness-Aware Question Answering for Intelligent Assistants</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em> </div> <div class="periodical"> <em>In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval</em>, Madrid, Spain, Aug 2022 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3477495.3531682" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/pathiyancherumanal2022dc_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> </div> <div class="abstract hidden"> <p>Conversational intelligent assistants, such as Amazon Alexa, Google Assistant, and Apple Siri, are a form of voice-only Question Answering (QA) system and have the potential to address complex information needs. However, at the moment they are mostly limited to answering with facts expressed in a few words. For example, when a user asks Google Assistant if coffee is good for their health, it responds by justifying why it is good for their health without shedding any light on the side effects coffee consumption might have citegao2020toward. Such limited exposure to multiple perspectives can lead to change in perceptions, preferences, and attitude of users, as well as to the creation and reinforcement of undesired cognitive biases. Getting such QA systems to provide a fair exposure to complex answers â including those with opposing perspectives â is an open research problem. In this research, I aim to address the problem of fairly exposing multiple perspectives and relevant answers to users in a multi-turn conversation without negatively impacting user satisfaction.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2021rmit" class="col-sm-8"> <div class="title">RMIT at TREC 2021 Fair Ranking Track</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>
3, Damiano Spina, Falk Scholer, and <span class="more-authors" title="click to view 1 more author" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '1 more author' ? 'W. Bruce Croft' : '1 more author'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">1 more author</span> </div> <div class="periodical"> <em>In The Thirtieth Text REtrieval Conference (TREC 2021) Proceedings</em>, Aug 2022 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="/assets/pdf/pathiyancherumanal2021rmit_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="/assets/pdf/pathiyancherumanal2021rmit_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>This report describes the data, the assumptions, methodology, and our results involved in the participation at the TREC 2021 Fair Ranking Track. While most of the fairness-aware re-ranking techniques require explicitly defining protected attributes, we tried to leverage the implicit features of the Wikimedia articles by using an implicit diversification technique to study the impact of diversification on a fair ranking problem.</p> </div> </div> </div> </li> </ol> <h2 class="bibliography">2021</h2> <ol class="bibliography"> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="pathiyancherumanal2021evaluatingfairness" class="col-sm-8"> <div class="title">Evaluating Fairness in Argument Retrieval</div> <div class="author"> <em>Sachin Pathiyan Cherumanal</em>, Damiano Spina, Falk Scholer, and <span class="more-authors" title="click to view 1 more author" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '1 more author' ? 'W. Bruce Croft' : '1 more author'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">1 more author</span> </div> <div class="periodical"> <em>In Proceedings of the 30th ACM International Conference on Information & Knowledge Management</em>, Virtual Event, Queensland, Australia, Aug 2021 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3459637.3482099" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/pathiyancherumanal2021evaluatingfairness_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> <a href="https://github.com/sachinpc1993/fair-arguments" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">Code</a> <a href="/assets/pdf/pathiyancherumanal2021evaluatingfairness_poster.pdf" class="btn btn-sm z-depth-0" role="button">Poster</a> </div> <div class="abstract hidden"> <p>Existing commercial search engines often struggle to represent different perspectives of a search query. Argument retrieval systems address this limitation of search engines and provide both positive (PRO) and negative (CON) perspectives about a userâs information need on a controversial topic (e.g., climate change). The effectiveness of such argument retrieval systems is typically evaluated based on topical relevance and argument quality, without taking into account the often differing number of documents shown for the argument stances (PRO or CON). Therefore, systems may retrieve relevant passages, but with a biased exposure of arguments. In this work, we analyze a range of non-stochastic fairness-aware ranking and diversity metrics to evaluate the extent to which argument stances are fairly exposed in argument retrieval systems.Using the official runs of the argument retrieval task Ttouché at CLEF 2020, as well as synthetic data to control the amount and order of argument stances in the rankings, we show that systems with the best effectiveness in terms of topical relevance are not necessarily the most fair or the most diverse in terms of argument stance. The relationships we found between (un)fairness and diversity metrics shed light on how to evaluate group fairness â in addition to topical relevance â in argument retrieval settings.</p> </div> </div> </div> </li> <li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="spina2021futureconversationsreport" class="col-sm-8"> <div class="title">Report on the future conversations workshop at CHIIR 2021</div> <div class="author"> Damiano Spina, Johanne R. Trippas, Paul Thomas, and <span class="more-authors" title="click to view 17 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '17 more authors' ? 'Hideo Joho, Katriina Byström, Leigh Clark, Nick Craswell, Mary Czerwinski, David Elsweiler, Alexander Frummet, Souvick Ghosh, Johannes Kiesel, Irene Lopatovska, Daniel McDuff, Selina Meyer, Ahmed Mourad, Paul Owoicho, Sachin Pathiyan Cherumanal, Daniel Russell, Laurianne Sitbon' : '17 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">17 more authors</span> </div> <div class="periodical"> <em>SIGIR Forum</em>, Jul 2021 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> <a href="https://doi.org/10.1145/3476415.3476421" class="btn btn-sm z-depth-0" role="button" rel="external nofollow noopener" target="_blank">DOI</a> <a href="/assets/pdf/spina2021futureconversationsreport_paper.pdf" class="btn btn-sm z-depth-0" role="button">PDF</a> </div> <div class="abstract hidden"> <p>The Future Conversations workshop at CHIIRâ21 looked to the future of search, recommendation, and information interaction to ask: where are the opportunities for conversational interactions? What do we need to do to get there? Furthermore, who stands to benefit?The workshop w
3as hands-on and interactive. Rather than a series of technical talks, we solicited position statements on opportunities, problems, and solutions in conversational search in all modalities (written, spoken, or multimodal). This paper -co-authored by the organisers and participants of the workshop- summarises the submitted statements and the discussions we had during the two sessions of the workshop. Statements discussed during the workshop are available at https://bit.ly/FutureConversations2021Statements.</p> </div> </div> </div> </li> </ol> <h2 class="bibliography">2017</h2> <ol class="bibliography"><li> <div class="row"> <div class="col col-sm-2 abbr"> </div> <div id="bindukr2017coldstartproblem" class="col-sm-8"> <div class="title">Reducing the Cold-User and Cold-Item Problem in Recommender System by Reducing the Sparsity of the Sparse Matrix and Addressing the Diversity-Accuracy Problem</div> <div class="author"> K. R. Bindu, Rhama Lalgudi Visweswaran, <em>Sachin Pathiyan Cherumanal</em>, and <span class="more-authors" title="click to view 2 more authors" onclick=" var element=$(this); element.attr('title', ''); var more_authors_text=element.text() == '2 more authors' ? 'Kundavai Devi Solai, Soundarya Gunasekaran' : '2 more authors'; var cursorPosition=0; var textAdder=setInterval(function(){ element.html(more_authors_text.substring(0, cursorPosition + 1)); if (++cursorPosition == more_authors_text.length){ clearInterval(textAdder); } }, '10'); ">2 more authors</span> </div> <div class="periodical"> <em>In Proceedings of International Conference on Communication and Networks</em>, Jul 2017 </div> <div class="periodical"> </div> <div class="links"> <a class="abstract btn btn-sm z-depth-0" role="button">Abs</a> </div> <div class="abstract hidden"> <p>Recommender Systems are a subclass of information filtering systems that seek to predict the preferences of a user or the preference that a user would give to an item. The most common problem faced by these systems is the lack of data. Such a situation leads to a matrix that is extremely sparse thus reducing the accuracy of prediction. Cold-start problem is one such problem that is faced by the recommender systems when a new user or a new item enters the system. We are hoping to reduce the cold-user and the cold-item problem by reducing the sparsity of the sparse matrix with the help of Iterative Local Least Squares algorithm and a hybrid of Heat Spreading algorithm and Probability Spreading algorithm.</p> </div> </div> </div> </li></ol> </div> </article> </div> </div> <footer class="fixed-bottom" role="contentinfo"> <div class="container mt-0"> © Copyright 2026 Sachin Pathiyan Cherumanal. Powered by <a href="https://jekyllrb.com/" target="_blank" rel="external nofollow noopener">Jekyll</a> with <a href="https://github.com/alshedivat/al-folio" rel="external nofollow noopener" target="_blank">al-folio</a> theme. Hosted by <a href="https://pages.github.com/" target="_blank" rel="external nofollow noopener">GitHub Pages</a>. Photos from <a href="https://unsplash.com" target="_blank" rel="external nofollow noopener">Unsplash</a>. Last updated: July 20, 2026. </div> </footer>
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