{"id":119,"date":"2023-06-20T12:50:04","date_gmt":"2023-06-20T12:50:04","guid":{"rendered":"https:\/\/sigir.org\/ictir2023\/?page_id=119"},"modified":"2023-06-20T12:50:04","modified_gmt":"2023-06-20T12:50:04","slug":"accepted-papers","status":"publish","type":"page","link":"https:\/\/sigir.org\/ictir2023\/accepted-papers\/","title":{"rendered":"Accepted Papers"},"content":{"rendered":"\n<ul class=\"wp-block-list\">\n<li><strong>Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank<\/strong>\n<ul class=\"wp-block-list\">\n<li>Tanya Chowdhury, Razieh Rahimi and James Allan<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Entity-Based Relevance Feedback for Document Retrieval<\/strong>\n<ul class=\"wp-block-list\">\n<li>Eilon Sheetrit, Fiana Raiber and Oren Kurland<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Assessment of the Quality of Topic Models for Information Retrieval Applications<\/strong>\n<ul class=\"wp-block-list\">\n<li>Meng Yuan, Pauline Lin, Lida Rashidi and Justin Zobel<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Content-Based Relevance Estimation in Retrieval Settings with Ranking-Incentivized Document Manipulations<\/strong>\n<ul class=\"wp-block-list\">\n<li>Ziv Vasilisky, Oren Kurland, Moshe Tennenholtz and Fiana Raiber<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Revisiting Condorcet Fusion<\/strong>\n<ul class=\"wp-block-list\">\n<li>Liron Tyomkin and Oren Kurland<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback<\/strong>\n<ul class=\"wp-block-list\">\n<li>Shashank Gupta, Harrie Oosterhuis and Maarten de Rijke<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Learning Aligned Cross-Modal and Cross-Product Embeddings for Generating the Topics of Shopping Needs<\/strong>\n<ul class=\"wp-block-list\">\n<li>Yi-Ru Tsai and Pu-Jen Cheng<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering<\/strong>\n<ul class=\"wp-block-list\">\n<li>Alireza Salemi, Mahta Rafiee and Hamed Zamani<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities<\/strong>\n<ul class=\"wp-block-list\">\n<li>Guglielmo Faggioli, Thibault Formal, Simon Lupart, Stefano Marchesin, Stephane Clinchant, Nicola Ferro and Benjamin Piwowarski<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Perspectives on Large Language Models for Relevance Judgment<\/strong>\n<ul class=\"wp-block-list\">\n<li>Guglielmo Faggioli, Laura Dietz, Charles Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein and Henning Wachsmuth<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>A Theoretical Analysis of Out-of-Distribution Detection in Multi-Label Classification<\/strong>\n<ul class=\"wp-block-list\">\n<li>Dell Zhang and Bilyana Taneva-Popova<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>The Effectiveness of Quantum Random Walk Model in Recommender Systems<\/strong>\n<ul class=\"wp-block-list\">\n<li>Hiroshi Wayama and Kazunari Sugiyama<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Balanced Knowledge Distillation with Contrastive Learning for Document Re-ranking<\/strong>\n<ul class=\"wp-block-list\">\n<li>Yingrui Yang, Shanxiu He, Yifan Qiao, Wentai Xie and Tao Yang<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Causal Learning for Simpson\u2019s Paradox Mitigation in Recommendation<\/strong>\n<ul class=\"wp-block-list\">\n<li>Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen and Yongfeng Zhang<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems<\/strong>\n<ul class=\"wp-block-list\">\n<li>Shuyi Wang and Guido Zuccon<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models<\/strong>\n<ul class=\"wp-block-list\">\n<li>Guido Zuccon, Harrisen Scells and Shengyao Zhuang<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Zero-shot Query Reformulation for Conversational Search<\/strong>\n<ul class=\"wp-block-list\">\n<li>Dayu Yang, Yue Zhang and Hui Fang<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>A is for Adele: An Offline Evaluation Metric for Instant Search<\/strong>\n<ul class=\"wp-block-list\">\n<li>Negar Arabzadeh, Oleksandra Kmet, Ben Carterette, Charles Clarke, Claudia Hauff and Praveen Chandar<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Clarifying Questions in Math Information Retrieval<\/strong>\n<ul class=\"wp-block-list\">\n<li>Behrooz Mansouri and Zahra Jahedibashiz<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Hierarchical Transformer-based Query by Multiple Documents<\/strong>\n<ul class=\"wp-block-list\">\n<li>Zhiqi Huang, Shahrzad Naseri, Hamed Bonab, Sheikh Muhammad Sarwar and James Allan<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>KALE: Using a K-Sparse Projector for Lexical Expansion<\/strong>\n<ul class=\"wp-block-list\">\n<li>Lu\u00eds Borges, Bruno Martins and Jamie Callan<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Learn to be Fair without Labels: a Distribution-based Learning Framework for Fair Ranking<\/strong>\n<ul class=\"wp-block-list\">\n<li>Fumian Chen and Hui Fang<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>CSurF: Sparse Lexical Retrieval through Contextualized Surface Forms<\/strong>\n<ul class=\"wp-block-list\">\n<li>Zhen Fan, Luyu Gao and Jamie Callan<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Retrieving Webpages using Online Discussions<\/strong>\n<ul class=\"wp-block-list\">\n<li>Kevin Ros, Matthew Jin, Jacob Levine and Chengxiang Zhai<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Dense Retrieval Adaptation using Target Domain Description<\/strong>\n<ul class=\"wp-block-list\">\n<li>Helia Hashemi, Yong Zhuang, Sachith Sri Ram Kothur, Srivas Prasad, Edgar Meij and Bruce Croft<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Exploring the Representation Power of SPLADE Models<\/strong>\n<ul class=\"wp-block-list\">\n<li>Joel Mackenzie, Shengyao Zhuang and Guido Zuccon<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Outcome-based Evaluation of Systematic Review Automation<\/strong>\n<ul class=\"wp-block-list\">\n<li>Wojciech Kusa, Guido Zuccon, Petr Knoth and Allan Hanbury<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning<\/strong>\n<ul class=\"wp-block-list\">\n<li>Roger Zhe Li, Juli\u00e1n Urbano and Alan Hanjalic<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Automatic Hint Generation (HG)<\/strong>\n<ul class=\"wp-block-list\">\n<li>Adam Jatowt, Calvin Gehrer and Michael F\u00e4rber<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Conversational Search with Random Walks over Entity Graphs<\/strong>\n<ul class=\"wp-block-list\">\n<li>Gustavo Gon\u00e7alves, Joao Magalhaes and Jamie Callan<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-119","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/pages\/119","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/comments?post=119"}],"version-history":[{"count":1,"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/pages\/119\/revisions"}],"predecessor-version":[{"id":121,"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/pages\/119\/revisions\/121"}],"wp:attachment":[{"href":"https:\/\/sigir.org\/ictir2023\/wp-json\/wp\/v2\/media?parent=119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}