Publications related to Preference-based Search using Example-Critiquing with Suggestions

Refining preference-based search results through Bayesian filtering

Preference-based search (PBS) is a popular approach for helping consumers find their desired items from online catalogs. Currently most PBS tools generate search results by a certain set of criteria based on preferences elicited from the current user durin ...

Association for Computing Machinery, New York, NY 10036-5701, United States2007

Preference-based organization interfaces: aiding user critiques in recommender systems

Pearl Pu Faltings, Chen Li

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 ...

Springer-Verlag2007

Explicit Passive Analysis in Electronic Catalogs

Martin Rajman, David Portabella Clotet

We consider example-critiquing systems that help people search for their most preferred item in a large catalog. We compare 6 existing approaches in terms of user or system-centric, implicit or explicit use of preferences, assumptions used and their behavi ...

2006

A comparative study of compound critique generation in conversational recommender systems

Pearl Pu Faltings, Jiyong Zhang

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 ...

Springer Verlag, Heidelberg, D-69121, Germany2006

Explicit Trade-off and Prospective Analysis in Electronic Catalogs

Martin Rajman, David Portabella Clotet

We consider example-critiquing systems that help people search for their most preferred item in a large electronic catalog. We analyze how such systems can help users in the framework of four existing example-critiquing approaches (RABBIT, FindMe, Incremen ...

2006

Stimulating preference expression using suggestions

Pearl Pu Faltings, Paolo Viappiani

Users often have to search for a most preferred item but do not know how to state their preferences in the language allowed by the system. Example-Critiquing has been proposed as a mixed-initiative technique for allowing them to construct their preference ...

American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States2005

Agile preference models based on soft constraints

Boi Faltings, Pearl Pu Faltings, Jiyong Zhang

An accurate model of the user's preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research ha ...

American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States2005

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Preference-based Search using Example-Critiquing with Suggestions

Refining preference-based search results through Bayesian filtering

Ontology filtering

Preference-based organization interfaces: aiding user critiques in recommender systems

Explicit Passive Analysis in Electronic Catalogs

A comparative study of compound critique generation in conversational recommender systems

Explicit Trade-off and Prospective Analysis in Electronic Catalogs

Stimulating preference expression using suggestions

Agile preference models based on soft constraints

Survey of Preference Elicitation Methods

Graph Chatbot

Chat with Graph Search

Refining preference-based search results through Bayesian filtering

Explicit Trade-off and Prospective Analysis in Electronic Catalogs

A comparative study of compound critique generation in conversational recommender systems

Explicit Passive Analysis in Electronic Catalogs

Preference-based organization interfaces: aiding user critiques in recommender systems

Ontology filtering

Survey of Preference Elicitation Methods

Stimulating preference expression using suggestions

Agile preference models based on soft constraints