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

TL;DR

This paper introduces a comprehensive dataset and platform for analyzing human search behavior in interactive information retrieval, enabling detailed study of individual differences and contextual factors affecting search performance.

Contribution

It provides a reusable, well-documented dataset with interaction logs, user characteristics, and a framework for conducting similar studies, advancing reproducibility in IIR research.

Findings

Dataset includes logs from 61 participants across 122 sessions

Reveals variability in search behavior based on individual traits

Supports development of user simulators for IIR systems

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.