Publications by Pearl Pu Faltings

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Online systems that help users select the most preferential item from a large electronic catalog are known as product search and recommender systems. Evaluation of various proposed technologies is essential for further development in this area. This paper ...

Communication networks are expected to offer a wide range of services to an increasingly large number of users, with a diverse range of quality of service. This calls for efficient control and management of these networks. In this paper, we address the problem of quality-of-service routing, more specifically the planning of bandwidth allocation to communication demands. Shortest path routing is the traditional technique applied to this problem. However, this can lead to poor network utilization and even con

Search goals are often too complex or poorly defined to be solved in a single query. While refining their search goals, users are likely to apply a variety of strategies, such as searching for more general or more specific concepts in reaction to the information and structures they encounter in the results. This is called opportunistic search. In this paper we describe how semantic fisheye views (SFEV) can be designed to effectively support this search process by enabling rapid, interactive exploration of t

Conversational recommender systems are designed to help users to more efficiently navigate complex product spaces by alternatively making recommendations and inviting users' feedback. Compound critiquing techniques provide an efficient way for users to fee ...

Each tweet is limited to 140 characters. This constraint surprisingly makes Twitter a more spontaneous platform to express our emotions. Detecting emotions and correctly classifying them automatically is an increasingly important task if we want to underst ...

As e-commerce has evolved into its second generation, where the available products are becoming more complex and their abundance is almost unlimited, the task of locating a desired choice has become too difficult for the average user. Therefore, more effor ...

The paper presents a qualitative study to explore the use of fitness trackers and their social functions in intergenerational settings. The study covered three phases of semi-structured interviews with older and younger adults during individual and interge ...

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 ...

We describe a user study evaluating two critiquing-based recommender agents based on three criteria: decision accuracy, decision effort, and user confidence. Results show that user-motivated critiques were more frequently applied and the example critiquing ...

Compound critiques allow users to simultaneously express directional preferences over several product attributes. Presenting the user with compound critiques is not a new idea. The original Find-Me Systems (e.g., Car Navigator) showed static compound criti ...

Understanding and predicting how people change their behavior after an intervention from time series data is an important task for health recommender systems. This task is especially challenging when the time series data is frequently sampled. In this pape ...

Location-based embedding is a fundamental problem to solve in location-based social network (LBSN). In this paper, we propose a geographical convolutional neural tensor network (GeoCNTN) as a generic embedding model. GeoCNTN first takes the raw location da ...

Our previous research indicates that using personality quizzes is a viable and promising way to build user profiles to recommend entertainment products. Based on these findings, our current research further investigates the feasibility of using personality quizzes to build user profiles not only for an active user but also his Or her friends. We first propose a general method that infers users' music preferences in terms of their personalities. Our in-depth user studies show that while active users perceive

Critiquing-based recommender systems elicit users' feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interacti ...

In this paper, we detail a method for domain specific, multi-category emotion recognition, based on human computation. We create an Amazon Mechanical Turk1 task that elicits emotion labels and phrase-emotion associations from the participants. Using the proposed method, we create an emotion lexicon, compatible with the 20 emotion categories of the Geneva Emotion Wheel. GEW is the first computational resource that can be used to assign emotion labels with such a high level of granularity. Our emotion annotat

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

Effective question-asking is a crucial component of a successful conversational chatbot. It could help the bots manifest empathy and render the interaction more engaging by demonstrating attention to the speaker's emotions. However, current dialog generati ...

As people increasingly rely on interactive decision support systems to choose products and make decisions, building effective interfaces for these systems becomes more and more challenging due to the explosion of on-line information, the initial incomplete user preference and user's cognitive and emotional limitations of information processing. How to accurately elicit user's preference hence becomes the main concern of current decision support systems. This paper is a survey of the typical preference elici

This paper presents AFEC, an automatically curated knowledge graph derived from everyday casual conversations. The graph’s encoded knowledge enhances conversational systems by modeling how people express acknowledgment, consolation, and empathy in social interactions. To construct a comprehensive and meaningful dataset, we curated a large-scale corpus from the r/CasualConversation subreddit. By extracting the first two turns of all conversations, we obtained 134K speaker nodes and 666K listener nodes. To il

A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model ...

Pearl Pu Faltings

Chat with Graph Search

Evaluating product search and recommender systems for E-commerce environments

IconoNET: a tool for automated bandwidth allocation planning

Opportunistic Search with Semantic Fisheye Views

Evaluating compound critiquing recommenders: A real-user study

Semi-Supervised Method for Multi-Category Emotion Recognition in Tweets

Experiments on the Preference-Based Organization Interface in Recommender Systems

Understanding intergenerational fitness tracking practices: 12 suggestions for design

Refining preference-based search results through Bayesian filtering

Evaluating critiquing-based recommencler agents

A comparison of two compound critiquing systems

Recommender System for Responsive Engagement of Senior Adults in Daily Activities

Geographical Feature Extraction for Entities in Location-based Social Networks

A Study on User Perception of Personality-Based Recommender Systems

Critiquing-based recommenders: survey and emerging trends

Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation

Key Qualities of Conversational Chatbots - the PEACE Model

A Taxonomy of Empathetic Questions in Social Dialogs

Survey of Preference Elicitation Methods

AFEC: A knowledge graph capturing social intelligence in casual conversations

Trust-inspiring explanation interfaces for recommender systems