ﻻ يوجد ملخص باللغة العربية
Conversational search (CS) has recently become a significant focus of the information retrieval (IR) research community. Multiple studies have been conducted which explore the concept of conversational search. Understanding and advancing research in CS requires careful and detailed evaluation. Existing CS studies have been limited to evaluation based on simple user feedback on task completion. We propose a CS evaluation framework which includes multiple dimensions: search experience, knowledge gain, software usability, cognitive load and user experience, based on studies of conversational systems and IR. We introduce these evaluation criteria and propose their use in a framework for the evaluation of CS systems.
We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain data without replying on explicitly encoded the rules but utilizing t
Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging given that any natural lang
Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several
Online experimentation platforms abstract away many of the details of experimental design, ensuring experimenters do not have to worry about sampling, randomisation, subject tracking, data collection, metric definition and interpretation of results.
The World Wide Web is a vast and continuously changing source of information where searching is a frequent, and sometimes critical, user task. Searching is not always the users primary goal but an ancillary task that is performed to find complementar