Publications by Pearl Pu Faltings

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Finding the optimal solution of a Multi-Attribute Decision Problem (MADP) is a key problem for electronic commerce systems. In this paper, we formally define the multi-attribute decision problem, and we report our survey of four different methods (soft-CSP framework, multi-attribute decision theory, CP-network, and Heuristic strategies) which potentially could be used to solve the MADP, and their advantages and disadvantages will be discussed respectively.

We present a cross-cultural user evaluation of an organization-based product recommender interface, by comparing it with the traditional list view. The results show that it performed significantly better, for all study participants, in improving on their c ...

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algor ...

This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users’ point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combin ...

Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the se ...

Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone as far as using implicit feedback indicators to understand users' interests. More than a decade after the emergence of recommender systems, the question whether users prefer them compared to stating their preferences explicitly, largely remains a subject of study. Even though some studies were found on users' acceptance and perceptions of

We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that ...

In many practical scenarios, users are faced with the problem of choosing the most preferred outcome from a large set of possibilities. As people are unable to sift through them manually, decisions support systems are often used to automatically find the optimal solution. A crucial requirement for such a system is to have an accurate model of the user's preferences. Studies have shown that people are usually unable to accurately state their preferences up front, but are greatly helped by seeing examples of

Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a road ...

Critiquing-based recommender systems provide an efficient way for users to navigate through complex product spaces even if they are not familiar with the domain details in e-commerce environments. While recent research has mainly concentrated on methods fo ...

In many interactive decision making scenarios there is often no solution that satisfies all of the user's preferences. The decision process can be helped by providing explanations. Relaxations show sets of consistent preferences and, thus, indicate which p ...

Conversational recommenders can help users find their most preferred item among a large range of options, a task that we call preference-based search. ...

In a service-oriented e-commerce environment, it is a crucial task to help consumers choose desired products efficiently from a huge amount of dynamically configured product candidates. Decision agents can be designed to provide interactive decision aids f ...

The social navigation types that include recommendations and guides were discussed. The systems goal was to increase visual awareness of social cues that exist in the information space, without levying the final decision on the user. Recommendation systems include computed information that help in guiding the users.

Current image retrieval systems offer either an exploratory search method through browsing and navigation or a direct search method based on specific queries. Combining both of these methods in a uniform framework allows users to formulate queries more naturally, since they are already acquainted with the contents of the database and with the notion of matching the machine would use to return results. We propose a multi-mode and integrated image retrieval system that offers the user quick and effective prev

Consumers are increasingly using the web to find answers to their health-related queries. Unfortunately, they often struggle with formulating the questions, further compounded by the burden of having to traverse long documents returned by the search engine ...

The problems associated with computerized search tools in online product search environments are discussed. The application of the Utility Theory, using a tool for the travel planning domain is illustrated. The features of Isy-Travel, a tool based on Smart Client Technology, are also discussed. The tool allows users to search for a flight itinerary according to their personal preferences and constraints.

Travel has many situations where context-aware computing can bring important benefits. In this paper, we describe an approach for integrating context-aware computing to a mobile travel assistant. Travel plans, generated using reality [2], are enriched within compact and powerful structures, called User Task Models. These structures are transferred to a mobile device enabling the support for the traveler during his trip.

Pearl Pu Faltings

Chat with Graph Search

Survey of solving multi-attribute decision problems

A Cross-Cultural User Evaluation of Product Recommender Interfaces

Evaluating recommender systems from the user's perspective: survey of the state of the art

A User-Centric Evaluation Framework for Recommender Systems

Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting Incongruity-Based Features for Humor Recognition

How Users Perceive and Appraise Personalised Recommendations

Hybrid critiquing-based recommender systems

How Users Perceive and Appraise Personalized Recommendations

Designing Example-critiquing Interaction

Trust building with explanation interfaces

A Visual Interface for Critiquing-based Recommender Systems

Representative explanations for over-constrained problems

Preference-based search with adaptive recommendations

Agile decision agent for service-oriented e-commerce systems

Social cues and awareness for recommendation systems

Integrated browsing and searching of large image collections

A survey of consumer health question answering systems

Effective interaction principles for online product search environments

Towards an Intelligent Mobile Travel Assistant

Preface to the special issue on user interfaces for recommender systems