Preference-based Search using Example-Critiquing with Suggestions

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Recommender systems have emerged as an effective decision tool to help users more easily and quickly find products that they prefer, especially in e-commerce environments. However, few studies have tried to understand how this technology has influenced the ...

User interaction with online geospatial systems is an inter-disciplinary research field that combines the two disciplines Human Computer Interaction (HCI) and geographic information sciences. In recent years online geospatial systems have been created for ...

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the ...

Retrieving information from archived meetings is a new domain of information retrieval that has received increasing attention in the past few years. Search in spontaneous spoken conversations has been recognized as more difficult than text-based document r ...

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Users' critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system's accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assi ...

Critiquing techniques provide an easy way for users to feedback their preferences over one or several attributes of the products in a conversational recommender system. While unit critiques only allow users to critique one attribute of the products each ti ...

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To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction mode ...

Preference-based Search using Example-Critiquing with Suggestions

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