Search Results for author:
Found 8 papers, 1 papers with code
A Conceptual Framework for Conversational Search and Recommendation: Conceptualizing Agent-Human Interactions During the Conversational Search Process
In this paper, we aim to develop a conceptual framework of the actions and intents of users and agents explaining how these actions enable the user to explore the search space and resolve their information need.
Accessibility in Information Retrieval
This paper introduces the concept of accessibility from the field of transportation planning and adopts it within the context of Information Retrieval (IR).
Measuring Bias in a Ranked List using Term-based Representations
With TExFAIR, we extend the AWRF framework to allow for the evaluation of fairness in settings with term-based representations of groups in documents in a ranked list.
The Influence of Presentation and Performance on User Satisfaction
Our research aims to bridge this gap by examining the relationship between query performance, presentation and user satisfaction.
TREC iKAT 2023: The Interactive Knowledge Assistance Track Overview
Conversational Information Seeking has evolved rapidly in the last few years with the development of Large Language Models providing the basis for interpreting and responding in a naturalistic manner to user requests.
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization
Query-by-document (QBD) retrieval is an Information Retrieval task in which a seed document acts as the query and the goal is to retrieve related documents -- it is particular common in professional search tasks.
Towards Building Economic Models of Conversational Search
Various conceptual and descriptive models of conversational search have been proposed in the literature -- while useful, they do not provide insights into how interaction between the agent and user would change in response to the costs and benefits of the different interactions.
Analysing Mixed Initiatives and Search Strategies during Conversational Search
In this paper, we present a model for conversational search -- from which we instantiate different observed conversational search strategies, where the agent elicits: (i) Feedback-First, or (ii) Feedback-After.
