Publications de Pearl Pu Faltings

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AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Personalizing behavior change (BC) strategies to motivate increased physical activity is especially important for the diverse older adult population. However, there is a lack of knowledge about how to profile older users to most effectively personalize BC ...

Critiquing-based recommenders do not require users to state all of their preferences upfront or rate a set of previously experienced products. Compared to other types of recommenders, they require relatively little user effort, especially initially, despite potential accuracy problems. On the other hand, they rely on a set of critiques to elicit users feedback in order to improve accuracy. Thus the better the critiques are, the more accurately and efficiently the system becomes in generating its recommendat

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 ...

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 ...

Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful revi ...

Open-domain chatbots engage with users in natural conversations to socialize and establish bonds. However, designing and developing an effective open-domain chatbot is challenging. It is unclear what qualities of a chatbot most correspond to users' expecta ...

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly ...

Personalization in e-commerce has so far been server-centric, requiring users to create a separate individual profile on each server that they like to access. As product information is increasingly coming from multiple and heterogeneous sources, the number of profiles becomes unmanageably large. We present SmartClient, a technology based on constraint programming where a thin but intelligent client provides personalized information access for its user. As the process can run on the user's side, it allows mu

This research was motivated by a desire to help office workers change their sedentary behavior because a prolonged sedentary posture increases the risks of developing musculoskeletal injuries and chronic diseases, thus threatening their physical and psycho ...

Exploratory search over a collection often requires users to iteratively apply a variety of strategies, such as searching for more general or more specific concepts in reaction to the information they encounter. Rich semantic models, such as WordNet, are p ...

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based sea ...

Effective opportunistic search in visual interfaces for large information collections, such as documents, Web pages, and annotated images, often requires domain knowledge that the information seeker does not have. Relevant semantic information, such as metadata and especially ontologies, are valuable resources for helping the user learn this domain knowledge. In this paper we describe an interactive "focus+context" visualization technique for integrating this semantic information into the representation of

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 ...

One difficulty often overlooked in information retrieval systems is that search criteria themselves are often poorly defined. People describe their information needs in many different ways and frequently change their goals depending on the current results of their search. We have investigated the hypothesis that overviews of the space of available solutions are a good way to remedy this situation. Our overview techniques allow users to get a feel for the meaning of categories through randomly chosen example

The internet presents people with an increasingly bewildering variety of choices. Online consumers have to rely on computerized search tools to find the most preferred option in a reasonable amount of time. Recommender systems address this problem by searc ...

One crucial task for e-commerce systems is to help buyers find products that not only satisfy their preferences but also reduce their search effort. Usually the amount of available products is far beyond the upper limit that any individual could process by ...

An increasing number of recommender systems enable conversational interaction to enhance the system's overall user experience (UX). However, it is unclear what qualities of a conversational recommender system (CRS) are essential to determine the success of ...

COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasible solutions. Two algorithm visualization techniques, Kaleidoscope and Lattice, and one visualization of n-dimensional data, MAP, are used to externalize the machine's problem solving strategies and the tradeoffs as a result of using these strategies. After a short training period, users are able to discover tactics to explore des

Consumer decision support systems (CDSSs) help online users make purchasing decisions in e-commerce Web sites. To more effectively compare the usefulness of the various functionalities and interface features of such systems, we have developed a simulation environment for decision tasks of any scale and structure. Furthermore, we have identified three criteria in an evaluation framework for assessing the quality of such CDSSs: users’ cognitive effort, preference expression effort, and decision accuracy. A se

Pearl Pu Faltings

Chattez avec Graph Search

Identifying a Motivational Profile for Older Adults Towards Increased Physical Activity

Critiquing Recommenders for Public Taste Products

A comparative study of compound critique generation in conversational recommender systems

Stimulating preference expression using suggestions

Prediction of Helpful Reviews using Emotions Extraction

PEACE: A Model of Key Social and EmotionalQualities of Conversational Chatbots

p Recommender systems: Past, present, future

Performance evaluation of consumer decision support systems

Personalized navigation of heterogeneous product spaces using SmartClient

HealthSit: Designing Posture-Based Interaction to Promote Exercise during Fitness Breaks

Concept expansion using semantic fisheye views

Integrating tradeoff support in product search tools for E-commerce sites

Searching with semantics: an interactive visualization technique for exploring an annotated image collection

Interaction design guidelines on critiquing-based recommender systems

Dynamic overview techniques for image retrieval

Increasing user decision accuracy using suggestions

Effort and accuracy analysis of choice strategies for electronic product catalogs

Key Qualities of Conversational Recommender Systems: From Users' Perspective

Interactive problem solving via algorithm visualization

Performance Evaluation of Consumer Decision Support Systems