Name
Abstract | ||
|---|---|---|
Understanding how to engage users is a critical question in many applications. Previous research has shown that unexpected or astonishing events can attract user attention, leading to positive outcomes such as engagement and learning. In this work, we investigate the similarity and differences in how people and algorithms rank the surprisingness of facts. Our crowdsourcing study, involving 106 participants, shows that computational models of surprise can be used to artificially induce surprise in humans. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Information Retrieval | Data science,Information retrieval,Computer science,Crowdsourcing,Computational model,Surprise,Perception |
DocType | Volume | Citations |
Journal | abs/1807.05906 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nalin Chhibber | 1 | 18 | 2.69 |
| Rohail Syed | 2 | 30 | 3.09 |
| Mengqiu Teng | 3 | 0 | 0.68 |
| joslin goh | 4 | 27 | 4.92 |
| Kevyn Collins-Thompson | 5 | 1121 | 65.69 |
| Edith Law | 6 | 348 | 43.02 |