Selected Publications

2024 | 2023 | 2022 | 2021 | 2020

2024

  • Zhang, Y. & Liu, J. (2024). Falling behind again? Characterizing and assessing older adults’ algorithm literacy in interactions with video recommendations. Journal of the Association for Information Science and Technology. (JASIST). [ Preprint in progress]
  • Jung, Y. J. & Liu, J. (2024). Toward a conceptual framework characterizing the interplay of interest development, information search, and knowledge construction (ISK) in children’s learning. Aslib Journal of Information Management. [Paper]
  • Wang, B. & Liu, J. (2024). Understanding users’ dynamic perceptions of search gain and cost in sessions: An expectation confirmation model. Journal of the Association for Information Science and Technology (JASIST). [Paper]
  • Zhang, Y. & Liu, J. (2024). Where do older adults’ mental models for video recommender systems come from: A qualitative study. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries. (JCDL). [Preprint in progress]
  • Chen, N., Liu, J., Dong, X. Liu, Q., Sakai, T. & Wu, X-M. (2024). AI can be cognitively biased: An exploratory study on threshold priming in LLM-based batch relevance assessment. In Proceedings of the 2nd International ACM SIGIR Conference on Information Retrieval in the Asia Pacific (SIGIR-AP). [Preprint]
  • Azzopardi, L. & Liu, J. (2024). Evaluating cognitive biases in conversational and generative IIR: A tutorial. In Proceedings of the ACM SIGIR Conference on Information Retrieval in the Asia Pacific. (SIGIR-AP). [Tutorial webpage in progress]
  • Wang, B. & Liu, J. (2024). Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics. In Proceedings of the ACM International Conference on the Theory of Information Retrieval. Washington D.C. USA. (ICTIR 2024). [Paper]
  • Liu, J. & Azzopardi, L. (2024). Search under Uncertainty: Cognitive Biases and Heuristics - A Tutorial on Testing, Mitigating and Accounting for Cognitive Biases in Search Experiments. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. Washington D.C. USA. (SIGIR 2024) [Tutorial]
  • Wang, B. (2024). A Proactive System for Supporting Users in Interactions with Large Language Models. In Proceedings of the ACM SIGIR Conference on Human Information Interaction and Retrieval. Sheffield, UK. (CHIIR 2024) [Extended Abstract][Slides]
  • Azzopardi, L. & Liu, J. (2024). Search under uncertainty: Cognitive biases and heuristics – Tutorial on modeling search interaction using behavioral economics. In Proceedings of the ACM SIGIR Conference on Human Information Interaction and Retrieval. Sheffield, UK. (CHIIR 2024) [Tutorial]
  • Wang, B., Liu, J., Karimnazarov, J., & Thompson, N. (2024). Task Supportive and Personalized Human-Large Language Model Interaction: A User Study. In Proceedings of the ACM SIGIR Conference on Human Information Interaction and Retrieval. Sheffield, UK. (CHIIR 2024) [Paper][Poster]

    • 2023

  • Markwald, M., Liu, J., & Yu, R. (2023). Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems. Information Retrieval Journal. [Paper]
  • Zhang, Y. & Liu, J. (2023). Deconstructing proxy health information-seeking behavior: A systematic review. Library and Information Science Research. 45(3). [Paper]
  • Wang, B., & Liu, J. (2023) Investigating the Role of In-situ User Expectations in Web Search. Information Processing & Management. [Paper][Code]
  • Liu, J. (2023). A behavioral economics approach to interactive information retrieval: Understanding and supporting boundedly rational users. Springer Nature. [Book abstract]
  • Wang, B. & Liu, J. (2023). Characterizing and early predicting user performance for adaptive search path recommendation. In Proceedings of the Annual Meeting of the Association for Information Science and Technology. [Best Information Behavior Conference Paper Award][Paper][Code]
  • Chen, N., Liu, J., & Sakai, T. (2023). A reference-dependent model for Web search evaluation: Understanding and measuring the experience of boundedly rational users. In Proceedings of the ACM Web Conference 2023. Austin, TX, USA. (TheWebConf2023) [Paper]
  • Liu, J. (2023). A two-sided fairness framework in search and recommendation. In Proceedings of the ACM SIGIR Conference on Human Information Interaction and Retrieval. Austin, TX, USA. (CHIIR 2023) [Paper]
  • Lei, J., Bu, Y., & Liu, J. (2023). Information retrieval research in academia and industry: A preliminary analysis of productivity, authorship, impact, and topic distribution. In iConference 2023. [Paper]

2022

  • Jiang, T. & Liu, J. Reflection on future directions: A systematic review of reported limitations and solutions in interactive information retrieval user studies. Aslib Journal of Information Management. [Paper]
  • Liu, J. (2022). Toward Cranfield-inspired reusability assessment in interactive information retrieval evaluation. Information Processing and Management. 59(5): 103007. [Paper]
  • Liu, J. & Han, F. (2022). 5-4 ≠ 4-3: On the uneven gaps between different levels of graded user satisfaction in interactive information retrieval evaluation. In Proceedings of 56th Hawaii International Conference on System Sciences (HICSS). Maui, HI. [Paper]
  • Jung, Y. J., & Liu, J. (2022). Children’s interest, search, and knowledge: A pilot analysis of a STEM maker workshop. In Proceedings of the 85th Annual Meeting of the Association for Information Science and Technology. 59(1). [Best Information Behavior Conference Poster Award][Paper]
  • Wang, B., & Liu, J. (2022). Investigating the relationship between in-situ user expectations and Web search behavior. In Proceedings of the 85th Annual Meeting of the Association for Information Science and Technology. 59(1). [Paper][Code]
  • Liu, J. & Han, F. (2022). Matching search result diversity with user diversity acceptance in Web search sessions. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval. 5 pages. Madrid, Spain. [Paper]
  • Ghosh, S. & Liu, J. (2022). OUHCIR at the NTCIR-16 Data Search 2 Task. In Proceedings of the NTCIR-16 Conference. 5 pages. Tokyo, Japan. [Paper]
  • Liu, J. & Shah, C. (2022). Leveraging user interaction signals and task state information in adaptively optimizing usefulness-oriented search sessions. In Proceedings of ACM/IEEE Joint Conference on Digital Libraries. 10 pages. Cologne, Germany. [Paper]
  • Brown, T. & Liu, J. (2022). A reference dependence approach to enhancing early prediction of session behavior and satisfaction. In Proceedings of ACM/IEEE Joint Conference on Digital Libraries. 5 pages. Cologne, Germany. [Paper]

2021

  • Liu, J. (2021). Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors. Information Processing & Management, 58(3), 102522. [Paper]
  • Liu, J., & Yu, R. (2021, October). State-Aware Meta-Evaluation of Evaluation Metrics in Interactive Information Retrieval. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 3258-3262). [Paper]
  • Liu, J., & Jung, Y. J. (2021, March). Interest Development, Knowledge Learning, and Interactive IR: Toward a State-based Approach to Search as Learning. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (pp. 239-248). [Paper]
  • Ghosh, S., & Ghosh, S. (2021, March). Classifying Speech Acts using Multi-channel Deep Attention Network for Task-oriented Conversational Search Agents. In Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (pp. 267-272). [Paper]
  • Ghosh, S., & Ghosh, S. (2021). “Do Users Need Human-like Conversational Agents?”–Exploring Conversational System Design Using Framework of Human Needs. [Paper]
  • Wang, B., & Liu, J. (2021). Extracting the Implicit Search States from Explicit Behavioral Signals in Complex Search Tasks. Proceedings of the Association for Information Science and Technology, 58(1), 854-856. [Paper][Code]

2020

  • Sarkar, S., Mitsui, M., Liu, J., & Shah, C. (2020). Implicit information need as explicit problems, help, and behavioral signals. Information Processing & Management, 57(2), 102069. [Paper]
  • Liu, J., & Han, F. (2020, July). Investigating Reference Dependence Effects on User Search Interaction and Satisfaction: A Behavioral Economics Perspective. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1141-1150). [Paper]
  • Liu, J., Sarkar, S., & Shah, C. (2020, March). Identifying and predicting the states of complex search tasks. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (pp. 193-202). [Paper]