Pearl Pu Faltings

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Publications associées (65)

p Recommender systems: Past, present, future

Pearl Pu Faltings

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 ...

AMER ASSOC ARTIFICIAL INTELL2021

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

Pearl Pu Faltings, Yubo Xie, Junze Li

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 ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

A Large-Scale Dataset for Empathetic Response Generation

Pearl Pu Faltings, Yubo Xie, Kalpani Anuradha Welivita

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 ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

User Expectations of Conversational Chatbots Based on Online Reviews

Pearl Pu Faltings, Ekaterina Svikhnushina, Alexandru Placinta

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 ...

ASSOC COMPUTING MACHINERY2021

Recommender System for Responsive Engagement of Senior Adults in Daily Activities

Pearl Pu Faltings, Igor Kulev, Yuan Lu

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 ...

SPRINGER INTERNATIONAL PUBLISHING AG2020

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

Pearl Pu Faltings, Yu Chen, Yuan Lu

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 ...

TAYLOR & FRANCIS INC2019

Geographical Feature Extraction for Entities in Location-based Social Networks

Pearl Pu Faltings

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 ...

ASSOC COMPUTING MACHINERY2018

Modeling the Impact of Modifiers on Emotional Statements

Pearl Pu Faltings, Valentina Sintsova, Margarita Bolívar Jiménez

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 ...

SPRINGER NATURE SWITZERLAND AG2018

EHR: a Sensing Technology Readiness Model for Lifestyle Changes

Pearl Pu Faltings, Yu Chen, Danni Le, Zerrin Yumak

Interest in developing user-centered sensing technologies for personalized behavior change has gained significant momentum. However, very little research work has been done to understand issues relative to user readiness and adoption of the sensing technol ...

Springer2017

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

Pearl Pu Faltings, Li Chen, Feng Wang

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 ...

Ios Press2017

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