Demo Data from VLDB 2026 / reviewers in the wild / expert
Hyung-Il Ahn
dblp:77/11205
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2Applied, interdisciplinary, general and emerging computing · 2Artificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 67% Web and social media mining · 33% | |
| Software engineering, system software, and programming languages
1 paper |
Services computing and microservices · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
customer relationship management |
0.2 | 1 | 2013 | A CRM system for social media: challenges and experiences · WWW 2013 |
Web and social media mining
social media analysis |
0.2 | 1 | 2013 | A CRM system for social media: challenges and experiences · WWW 2013 |
Data mining
text mining |
0.2 | 1 | 2013 | A CRM system for social media: challenges and experiences · WWW 2013 |
Services computing and microservices
enterprise systems |
0.0 | 1 | 2013 | A CRM system for social media: challenges and experiences · WWW 2013 |
Methods — techniques the papers use, named apart from their topics
conversation mining · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Measuring Affective-Cognitive Experience and Predicting Market SuccessabstractWe present a new affective-behavioral-cognitive (ABC) framework to measure the usual cognitive self-report information and behavioral information, together with affective information while a customer makes repeated selections in a random-outcome two-option decision task to obtain their preferred product. The affective information consists of human-labeled facial expression valence taken from two contexts: one where the facial valence is associated with affective wanting, and the other with affective liking. The new “affective wanting” measure is made by setting up a condition where the person shows desire to receive one of two products, and we measure if the face looks satisfied or disappointed when each of the products arrives. The “affective liking” measure captures facial expressions after sampling a product. The ABC framework is tested in a real-world beverage taste experiment, comparing two similar products that actually went to market, where we know the market outcomes. We find that the affective measure provides significant improvement over the cognitive measure, increasing the discriminability between the two similar products, making it easier to tell which is most preferred using a small number of people. We also find that the new facial valence “affective wanting” measure provides a significant boost in discrimination and accuracy. Hyung-Il Ahn, Rosalind W. Picard |
IEEE Trans. Affect. Comput. | 1 |
| 2013 | Content Analytics System for Social Customer Relationship Management
Meena Nagarajan, Danish Contractor, Stephen Dill, Jitendra Ajmera, Hyung-Il Ahn, Ashish Verma 0001, Matthew Denesuk |
ICWSM | 5 |
| 2013 | A CRM system for social media: challenges and experiencesabstractThe social Customer Relationship Management (CRM) landscape is attracting significant attention from customers and enterprises alike as a sustainable channel for tracking, managing and improving customer relations. Enterprises are taking a hard look at this open, unmediated platform because the community effect generated on this channel can have a telling effect on their brand image, potential market opportunity and customer loyalty. In this work we present our experiences in building a system that mines conversations on social platforms to identify and prioritize those posts and messages that are relevant to enterprises. The system presented in this work aims to empower an agent or a representative in an enterprise to monitor, track and respond to customer communication while also encouraging community participation. Jitendra Ajmera, Hyung-Il Ahn, Meena Nagarajan, Ashish Verma 0001, Danish Contractor, Stephen Dill, Matthew Denesuk |
WWW | 2 |