Event attendance prediction in social networks / Xiaomei Zhang, Guohong Cao.
This volume focuses on predicting users' attendance at a future event at specific time and location based on their common interests. Event attendance prediction has attracted considerable attention because of its wide range of potential applications. By predicting event attendance, events that...
Saved in:
| Online Access: |
Full Text (via Springer) |
|---|---|
| Main Authors: | , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2021.
|
| Series: | SpringerBriefs in statistics.
|
| Subjects: |
MARC
| LEADER | 00000cam a2200000xi 4500 | ||
|---|---|---|---|
| 001 | b12087846 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 220107s2021 sz a ob 000 0 eng d | ||
| 005 | 20240423173330.8 | ||
| 019 | |a 1290814276 |a 1290840869 | ||
| 020 | |a 9783030892623 |q (electronic bk.) | ||
| 020 | |a 303089262X |q (electronic bk.) | ||
| 020 | |z 9783030892616 |q (print) | ||
| 020 | |z 3030892611 | ||
| 024 | 7 | |a 10.1007/978-3-030-89262-3 | |
| 035 | |a (OCoLC)spr1291272118 | ||
| 035 | |a (OCoLC)1291272118 |z (OCoLC)1290814276 |z (OCoLC)1290840869 | ||
| 037 | |a spr978-3-030-89262-3 | ||
| 040 | |a GW5XE |b eng |e rda |e pn |c GW5XE |d GW5XE |d YDX |d EBLCP |d OCLCO | ||
| 049 | |a GWRE | ||
| 050 | 4 | |a QA76.9.D343 | |
| 100 | 1 | |a Zhang, Xiaomei, |e author. |0 http://id.loc.gov/authorities/names/n2012041419. | |
| 245 | 1 | 0 | |a Event attendance prediction in social networks / |c Xiaomei Zhang, Guohong Cao. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c 2021. | |
| 300 | |a 1 online resource (viii, 54 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a SpringerBriefs in statistics, |x 2191-5458. | |
| 505 | 0 | |a Introduction -- Related Work -- Data Collection -- Event Attendance Prediction -- Performance Evaluations -- Conclusions and Future Research Directions. | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a This volume focuses on predicting users' attendance at a future event at specific time and location based on their common interests. Event attendance prediction has attracted considerable attention because of its wide range of potential applications. By predicting event attendance, events that better fit users' interests can be recommended, and personalized location-based or topic-based services related to the events can be provided to users. Moreover, it can help event organizers estimating the event scale, identifying conflicts, and help manage resources. This book first surveys existing techniques on event attendance prediction and other related topics in event-based social networks. It then introduces a context-aware data mining approach to predict the event attendance by learning how users are likely to attend future events. Specifically, three sets of context-aware attributes are identified by analyzing users' past activities, including semantic, temporal, and spatial attributes. This book illustrates how these attributes can be applied for event attendance prediction by incorporating them into supervised learning models, and demonstrates their effectiveness through a real-world dataset collected from event-based social networks. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed January 7, 2022) | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073. | |
| 650 | 0 | |a Context-aware computing. |0 http://id.loc.gov/authorities/subjects/sh2008007436. | |
| 650 | 0 | |a Special events |x Statistical methods. | |
| 700 | 1 | |a Cao, Guohong, |e author. |0 http://id.loc.gov/authorities/names/ns2014001305. | |
| 776 | 0 | 8 | |c Original |z 3030892611 |z 9783030892616 |w (OCoLC)1268326592. |
| 830 | 0 | |a SpringerBriefs in statistics. |0 http://id.loc.gov/authorities/names/no2011109398. | |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-89262-3 |z Full Text (via Springer) |
| 907 | |a .b12087846x |b 03-01-22 |c 02-04-22 | ||
| 998 | |a web |b 02-28-22 |c b |d b |e - |f eng |g sz |h 0 |i 1 | ||
| 907 | |a .b12087846x |b 02-28-22 |c 02-04-22 | ||
| 944 | |a MARS - RDA ENRICHED | ||
| 915 | |a I | ||
| 956 | |a Springer e-books | ||
| 956 | |b Springer Nature - Springer Mathematics and Statistics eBooks 2021 English International | ||
| 999 | f | f | |i 3ee55fa3-2398-5bb8-96a5-e920d1b8d760 |s f37b775d-7900-5638-bb4d-4dd0596a8a37 |
| 952 | f | f | |p Can circulate |a University of Colorado Boulder |b Online |c Online |d Online |e QA76.9.D343 |h Library of Congress classification |i Ebooks, Prospector |n 1 |