Abstract
In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in many domains, present machine-human (hybrid) algorithms that screen items efficiently and estimate the gain over human-only or machine-only screening in terms of performance and cost.
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 95-96 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781450356404 |
| DOIs | |
| Publication status | Published - 23 Apr 2018 |
| Externally published | Yes |
| Event | 27th International World Wide Web, WWW 2018 - Lyon, France Duration: 23 Apr 2018 → 27 Apr 2018 |
Publication series
| Name | The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 |
|---|
Conference
| Conference | 27th International World Wide Web, WWW 2018 |
|---|---|
| Country | France |
| City | Lyon |
| Period | 23.4.18 → 27.4.18 |
Keywords
- crowdsourcing
- human computation
- hybrid classification
- machine learning
ASJC Scopus subject areas
- Computer Networks and Communications
- Software
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Cite this
Krivosheev, E., Harandizadeh, B., Casati, F., & Benatallah, B. (2018). Crowd-Machine Collaboration for Item Screening. In The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 (pp. 95-96). (The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018). Association for Computing Machinery, Inc. https://doi.org/10.1145/3184558.3186946