File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TII.2019.2926885
- Scopus: eid_2-s2.0-85078701702
- WOS: WOS:000521337000038
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Modeling Human Activity with Seasonality Bursty Dynamics
Title | Modeling Human Activity with Seasonality Bursty Dynamics |
---|---|
Authors | |
Keywords | temporal human behavior Complex network video game industry temporal point processes |
Issue Date | 2020 |
Citation | IEEE Transactions on Industrial Informatics, 2020, v. 16, n. 2, p. 1130-1139 How to Cite? |
Abstract | © 2005-2012 IEEE. The public's purchase incentive increases dramatically during the holiday season and subsequently returns to normal levels. This seasonality is common in various scenarios and highlights the following questions: how does the public's purchase incentive fluctuate over the course of a year? Which factors are conducive to this seasonal behavior and how can they be modeled? In this paper, we propose a model that explicitly integrates temporal point process theory with the construction of a networked community, to describe the dynamics of collective action propagation with seasonal fluctuation. Furthermore, a database is constructed of sales records for 21 video game consoles and 13 237 video games in France, Germany, Japan, the U.K., the USA, and worldwide from 1989 to 2018. Experimental results suggest that peak desire always appears in the holiday season about one week before Christmas and is about four times higher than consumption desire in a normal period in all areas. |
Persistent Identifier | http://hdl.handle.net/10722/281378 |
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 4.420 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wen, Quansi | - |
dc.contributor.author | Zhan, Choujun | - |
dc.contributor.author | Gao, Ying | - |
dc.contributor.author | Hu, Xiping | - |
dc.contributor.author | Ngai, Edith | - |
dc.contributor.author | Hu, Bin | - |
dc.date.accessioned | 2020-03-13T10:37:43Z | - |
dc.date.available | 2020-03-13T10:37:43Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Industrial Informatics, 2020, v. 16, n. 2, p. 1130-1139 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/281378 | - |
dc.description.abstract | © 2005-2012 IEEE. The public's purchase incentive increases dramatically during the holiday season and subsequently returns to normal levels. This seasonality is common in various scenarios and highlights the following questions: how does the public's purchase incentive fluctuate over the course of a year? Which factors are conducive to this seasonal behavior and how can they be modeled? In this paper, we propose a model that explicitly integrates temporal point process theory with the construction of a networked community, to describe the dynamics of collective action propagation with seasonal fluctuation. Furthermore, a database is constructed of sales records for 21 video game consoles and 13 237 video games in France, Germany, Japan, the U.K., the USA, and worldwide from 1989 to 2018. Experimental results suggest that peak desire always appears in the holiday season about one week before Christmas and is about four times higher than consumption desire in a normal period in all areas. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Industrial Informatics | - |
dc.subject | temporal human behavior | - |
dc.subject | Complex network | - |
dc.subject | video game industry | - |
dc.subject | temporal point processes | - |
dc.title | Modeling Human Activity with Seasonality Bursty Dynamics | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TII.2019.2926885 | - |
dc.identifier.scopus | eid_2-s2.0-85078701702 | - |
dc.identifier.volume | 16 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 1130 | - |
dc.identifier.epage | 1139 | - |
dc.identifier.eissn | 1941-0050 | - |
dc.identifier.isi | WOS:000521337000038 | - |
dc.identifier.issnl | 1551-3203 | - |