LISP -- A Rich Interaction Dataset and Loggable Interactive Search Platform

2026-01-14

AI Summary

This work addresses the lack of reproducible, fine-grained datasets that incorporate individual user differences in interactive information retrieval research. To bridge this gap, the authors present an open-source dataset comprising 61 users and 122 search sessions, which systematically integrates multidimensional user attributes—such as perceived speed, interests, and search experience—with fine-grained interaction logs. Accompanying this dataset, the study introduces a web-based experimental platform, a structured logging framework, and an open dissemination protocol, collectively offering a comprehensive and reusable research infrastructure. Through illustrative analyses, the authors demonstrate the dataset’s utility in modeling user behavior and developing simulation tools, thereby significantly advancing reproducible and context-aware research in interactive retrieval.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

We present a reusable dataset and accompanying infrastructure for studying human search behavior in Interactive Information Retrieval (IIR). The dataset combines detailed interaction logs from 61 participants (122 sessions) with user characteristics, including perceptual speed, topic-specific interest, search expertise, and demographic information. To facilitate reproducibility and reuse, we provide a fully documented study setup, a web-based perceptual speed test, and a framework for conducting similar user studies. Our work allows researchers to investigate individual and contextual factors affecting search behavior, and to develop or validate user simulators that account for such variability. We illustrate the datasets potential through an illustrative analysis and release all resources as open-access, supporting reproducible research and resource sharing in the IIR community.

Problem

• Interactive Information Retrieval
• human search behavior
• user simulation
• individual differences
• interaction logs

Innovation

• Interactive Information Retrieval
• User Behavior Dataset
• Reproducible Research Infrastructure
• User Simulator
• Perceptual Speed