Name
Abstract | ||
|---|---|---|
In this paper, we present Expediting Expertise, a system designed to provide structured support to the otherwise informal process of social learning in the enterprise. It employs a data-driven approach where online content is automatically analyzed and categorized into relevant topics, topic-specific user expertise is calculated by comparing the models of individual users against those of the experts, and personalized recommendation of learning activities is created accordingly to facilitate expertise development. The system's UI is designed to provide users with ongoing feedback of current expertise, progress, and comparison with others. Learning recommendation is visualized with an interactive treemap which presents estimated return on investment and distance to current expertise for each recommended learning activity. Evaluation of the system showed very positive results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2557500.2557539 | IUI |
Keywords | Field | DocType |
current expertise,ongoing feedback,expediting expertise,informal process,data-driven approach,interactive treemap,topic-specific user expertise,social learning,informal social learning,individual user,expertise development,personalized recommendation,informal,assessment,social | World Wide Web,Return on investment,Computer science,Expediting,Knowledge management,Social learning,Multimedia | Conference |
Citations | PageRank | References |
5 | 0.50 | 14 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jennifer C. Lai | 1 | 1675 | 569.09 |
| Jie Lu | 2 | 76 | 7.62 |
| Shimei Pan | 3 | 684 | 64.41 |
| Danny Soroker | 4 | 188 | 19.90 |
| Mercan Topkara | 5 | 267 | 19.51 |
| Justin D. Weisz | 6 | 111 | 19.46 |
| Jeff Boston | 7 | 13 | 2.69 |
| Jason Crawford | 8 | 40 | 4.18 |