Towards Humane Feedback Mechanisms in Exploratory Search

Sørig, Esben; Collignon, Nicolas; Fiebrink, Rebecca and Kando, Noriko. 2019. 'Towards Humane Feedback Mechanisms in Exploratory Search'. In: CHI 2019 Workshop on Emerging Perspectives in Human-Centered Machine Learning. Glasgow, United Kingdom 4 May 2019. [Conference or Workshop Item]

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Abstract or Description

Machine learning (ML) plays a central role in modern information retrieval (IR) systems. We argue that, in IR systems for multi-session exploratory search, there are unexploited opportunities for IR document ranking models to leverage users’ knowledge about the search task to better support users’ search needs. Specifically, we propose a method to enable users to adapt an IR document ranking model according to their information needs, using an interface that supports search strategies and methods for engaging with documents known to be useful when people explore new or complex domains of knowledge. We also discuss the major challenges in creating human-centered machine learning models and interfaces for exploratory search.

Item Type:

Conference or Workshop Item (Poster)

Additional Information:

This work was supported by Microsoft Research through its PhD Scholarship Programme and by the National Institute of Informatics (Japan). JSPS KAKENHI Grant Numbers JP16H01756.


Exploration, Relevance Feedback, Interactive Search, HCI, Human-Centered Machine Learning

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5 March 2019Accepted
4 May 2019Published

Event Location:

Glasgow, United Kingdom

Date range:

4 May 2019

Item ID:


Date Deposited:

05 Jun 2019 12:03

Last Modified:

03 Jun 2024 09:55


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