ICTIR 2023

ICTIR2023

The 9th ACM SIGIR / The 13th International Conference on the Theory of Information Retrieval Held jointly with the SIGIR 2023 conference

Accepted Papers

  • Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
    • Tanya Chowdhury, Razieh Rahimi and James Allan
  • Entity-Based Relevance Feedback for Document Retrieval
    • Eilon Sheetrit, Fiana Raiber and Oren Kurland
  • Assessment of the Quality of Topic Models for Information Retrieval Applications
    • Meng Yuan, Pauline Lin, Lida Rashidi and Justin Zobel
  • Content-Based Relevance Estimation in Retrieval Settings with Ranking-Incentivized Document Manipulations
    • Ziv Vasilisky, Oren Kurland, Moshe Tennenholtz and Fiana Raiber
  • Revisiting Condorcet Fusion
    • Liron Tyomkin and Oren Kurland
  • A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback
    • Shashank Gupta, Harrie Oosterhuis and Maarten de Rijke
  • Learning Aligned Cross-Modal and Cross-Product Embeddings for Generating the Topics of Shopping Needs
    • Yi-Ru Tsai and Pu-Jen Cheng
  • Pre-Training Multi-Modal Dense Retrievers for Outside-Knowledge Visual Question Answering
    • Alireza Salemi, Mahta Rafiee and Hamed Zamani
  • Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities
    • Guglielmo Faggioli, Thibault Formal, Simon Lupart, Stefano Marchesin, Stephane Clinchant, Nicola Ferro and Benjamin Piwowarski
  • Perspectives on Large Language Models for Relevance Judgment
    • Guglielmo Faggioli, Laura Dietz, Charles Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein and Henning Wachsmuth
  • A Theoretical Analysis of Out-of-Distribution Detection in Multi-Label Classification
    • Dell Zhang and Bilyana Taneva-Popova
  • The Effectiveness of Quantum Random Walk Model in Recommender Systems
    • Hiroshi Wayama and Kazunari Sugiyama
  • Balanced Knowledge Distillation with Contrastive Learning for Document Re-ranking
    • Yingrui Yang, Shanxiu He, Yifan Qiao, Wentai Xie and Tao Yang
  • Causal Learning for Simpson’s Paradox Mitigation in Recommendation
    • Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen and Yongfeng Zhang
  • An Analysis of Untargeted Poisoning Attack and Defense Methods for Federated Online Learning to Rank Systems
    • Shuyi Wang and Guido Zuccon
  • Beyond CO2 Emissions: The Overlooked Impact of Water Consumption of Information Retrieval Models
    • Guido Zuccon, Harrisen Scells and Shengyao Zhuang
  • Zero-shot Query Reformulation for Conversational Search
    • Dayu Yang, Yue Zhang and Hui Fang
  • A is for Adele: An Offline Evaluation Metric for Instant Search
    • Negar Arabzadeh, Oleksandra Kmet, Ben Carterette, Charles Clarke, Claudia Hauff and Praveen Chandar
  • Clarifying Questions in Math Information Retrieval
    • Behrooz Mansouri and Zahra Jahedibashiz
  • Hierarchical Transformer-based Query by Multiple Documents
    • Zhiqi Huang, Shahrzad Naseri, Hamed Bonab, Sheikh Muhammad Sarwar and James Allan
  • KALE: Using a K-Sparse Projector for Lexical Expansion
    • Luís Borges, Bruno Martins and Jamie Callan
  • Learn to be Fair without Labels: a Distribution-based Learning Framework for Fair Ranking
    • Fumian Chen and Hui Fang
  • CSurF: Sparse Lexical Retrieval through Contextualized Surface Forms
    • Zhen Fan, Luyu Gao and Jamie Callan
  • Retrieving Webpages using Online Discussions
    • Kevin Ros, Matthew Jin, Jacob Levine and Chengxiang Zhai
  • Dense Retrieval Adaptation using Target Domain Description
    • Helia Hashemi, Yong Zhuang, Sachith Sri Ram Kothur, Srivas Prasad, Edgar Meij and Bruce Croft
  • Exploring the Representation Power of SPLADE Models
    • Joel Mackenzie, Shengyao Zhuang and Guido Zuccon
  • Outcome-based Evaluation of Systematic Review Automation
    • Wojciech Kusa, Guido Zuccon, Petr Knoth and Allan Hanbury
  • Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
    • Roger Zhe Li, Julián Urbano and Alan Hanjalic
  • Automatic Hint Generation (HG)
    • Adam Jatowt, Calvin Gehrer and Michael Färber
  • Conversational Search with Random Walks over Entity Graphs
    • Gustavo Gonçalves, Joao Magalhaes and Jamie Callan