Publications by Pearl Pu Faltings

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Preference-based search is the problem of finding an item that matches best with a user's preferences. User studies show that example-based tools for preference-based search can achieve significantly higher accuracy when they are complemented with suggesti ...

Recommender systems (RSs), as used by Netflix, YouTube, or Amazon, are one of the most compelling success stories of AI. Enduring research activity in this area has led to a continuous improvement of recommendation techniques over the years, and today's RS ...

Humans use a variety of modifiers to enrich communications with one another. While this is a deliberate subtlety in our language, the presence of modifiers can cause problems for emotion analysis by machines. Our research objective is to understand and com ...

In recent years much research work has been dedicated to detecting user activity patterns from sensor data such as location, movement and proximity. However, how daily activities are correlated to people's happiness (such as their satisfaction from work an ...

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 ...

Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sen ...

Recent development in NLP shows a strong trend towards refining pre-trained models with a domain-specific dataset. This is especially the case for response generation where emotion plays an important role. However, existing empathetic datasets remain small ...

Recommender systems have increasingly become popular in various web environments (such as e-commerce and social media) for automatically generating items that match to individual users' personal interests. Among different types of recommender systems that ...

The demands of the modern world are increasingly responsible for causing psychological burdens and bringing adverse impacts on our mental health. As a result, neural conversational agents with empathetic responding and distress management capabilities have ...

Two music recommender websites, Pandora (a content-based recommender) and Last.fm (a rating-based social recommender), were compared side-by-side in a within-subject user study involving 64 participants. The main objective was to investigate users’ initial ...

Visual thinking tools are visualization-enabled mixed initiative systems that empower people in solving complex problems by engaging them in the entire resolution process, suggesting appropriate actions with visual cues, and reducing their cognitive load with visual representations of their tasks. At the same time, the visual interaction style provides an alternative to the dialog-based model employed in most mixed-initiative (MI) systems. Visual thinking tools avoid complex analyses of turn taking, and put

In electronic commerce, a satisfying buyer experience is a key competitive element. We show new techniques for better adapting interaction with an electronic catalog system to actual buying behavior. Our model replaces the sequential separation of needs identification and product brokering with a conversion in which both processes occur simultaneously. This conversation supports the buyer in formulating his or her needs, and in deciding which criteria to apply in selecting a product to buy. We have experime

Maintaining a physically active lifestyle is important for diabetic and obese patients, but how to motivate them to exercise and engage them in the long run remains a challenging issue. We aim to motivate their activities using fitness trackers and prevent ...

Open-domain chatbots that can engage in a conversation on any topic received significant attention in the last several years, which opened opportunities for studying user interaction with them. Drawing from reviews of chatbots posted on Google Play, we exp ...

Search goals are often too complex or poorly defined to be solved with a single query in a Web-based information environment. In this paper we describe how we use semantic fisheye views (SFEVs) to effectively support opportunistic search and browsing strategies in an annotated image collection. We have developed a SFEV prototype enabling rapid, interactive exploration using both keyword similarity and semantic relationships derived using WordNet. SFEVs visually emphasize and increase the detail of informati

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user ...

Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports eve ...

People frequently use the world-wide web to find their most preferred item among a large range of options. We call this task preference-based search. The most common tool for preference-based search on the WWW today obtains users' preferences by asking them to fill in a form. It then returns a list of items that most closely match these preferences. Recently, several researchers have proposed tools for preference-based search that elicit preferences from the critiques a user actively makes on examples shown

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 ...

Many information systems are used in a problem solving context. Examples are travel planning systems, catalogs in electronic commerce, or agenda planning systems. They can be made more useful by integrating problem-solving capabilities into the information systems. This poses the challenge of scaleability: when hundreds of users access a server at the same time, it is important to avoid excessive computational load. We present the concept of SmartClients: lightweight problem-solving agents based on constrai

Pearl Pu Faltings

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The lookahead principle for preference elicitation: Experimental results

Recommender systems: Trends and frontiers

Modeling the Impact of Modifiers on Emotional Statements

Predictors of Life Satisfaction based on Daily Activities from Mobile Sensor Data

Eye-Tracking Product Recommenders' Usage

A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

A Large-Scale Dataset for Empathetic Response Generation

Investigating users' eye movement behavior in critiquing-based recommender systems

HEAL: A Knowledge Graph for Distress Management Conversations

User Technology Adoption Issues in Recommender Systems

Design visual thinking tools for mixed initiative systems

Enriching buyers' experiences: the SmartClient approach

Peer Influence on the Engagement of Fitness Tracker Usage: A Diabetes and Obesity Study

User Expectations of Conversational Chatbots Based on Online Reviews

Opportunistic search with semantic fisheye views

User-Involved Preference Elicitation for Product Search and Recommender Systems

Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets

Evaluating Preference-based Search Tools: a Tale of Two Approaches

Agile preference models based on soft constraints

SmartClients: Constraint satisfaction as a paradigm for scaleable intelligent information systems