Bis zu 50 % günstiger als neu 3 Jahre rebuy Garantie Professionelles Refurbishment
ElektronikMedien
Tipps & News
AppleAlle anzeigen
TabletsAlle anzeigen
HandyAlle anzeigen
Fairphone
AppleAlle anzeigen
iPhone Air Generation
GoogleAlle anzeigen
Pixel Fold
HonorAlle anzeigen
HuaweiAlle anzeigen
Honor SerieY-Serie
NothingAlle anzeigen
OnePlusAlle anzeigen
OnePlus 11 GenerationOnePlus 12 Generation
SamsungAlle anzeigen
Galaxy XcoverWeitere Modelle
SonyAlle anzeigen
Weitere Modelle
XiaomiAlle anzeigen
Weitere Modelle
Tablets & eBook ReaderAlle anzeigen
Google
AppleAlle anzeigen
HuaweiAlle anzeigen
MatePad Pro Serie
MicrosoftAlle anzeigen
XiaomiAlle anzeigen
Kameras & ZubehörAlle anzeigen
ObjektiveAlle anzeigen
System & SpiegelreflexAlle anzeigen
WearablesAlle anzeigen
Fitness TrackerAlle anzeigen
SmartwatchesAlle anzeigen
Xiaomi
Konsolen & ZubehörAlle anzeigen
Lenovo Legion GoMSI Claw
NintendoAlle anzeigen
Nintendo Switch Lite
PlayStationAlle anzeigen
XboxAlle anzeigen
Audio & HiFiAlle anzeigen
KopfhörerAlle anzeigen
FairphoneGoogle
LautsprecherAlle anzeigen
Beats by Dr. DreGoogleYamahatonies
iPodAlle anzeigen

Handgeprüfte Gebrauchtware

Bis zu 50 % günstiger als neu

Der Umwelt zuliebe

Spatio-Temporal Recommendation in Social Media

Hongzhi Yin, Bin Cui (Taschenbuch, Englisch)

Keine Bewertungen vorhanden
Optischer Zustand
Beschreibung
This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users’ behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users’ behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.
Dieses Produkt haben wir gerade leider nicht auf Lager.
ab 42,99 €
Derzeit nicht verfügbar
Derzeit nicht verfügbar

Handgeprüfte Gebrauchtware

Bis zu 50 % günstiger als neu

Der Umwelt zuliebe

Technische Daten


Erscheinungsdatum
25.05.2016
Sprache
Englisch
EAN
9789811007477, 9789811007477
Herausgeber
Springer Singapore
Serien- oder Bandtitel
SpringerBriefs in Computer Science
Sonderedition
Nein
Autor
Hongzhi Yin, Bin Cui
Seitenanzahl
114
Einbandart
Taschenbuch
Autorenporträt
Dr. Hongzhi Yin has been an ARC DECRA fellow in the School of Information Technology and Electrical Engineering (ITEE), at The University of Queensland (UQ), and he received his PhD degree from Peking University in July 2014. His research interests include Recommender System and User Modeling, Social Media Mining and Management, Location-based Social Network Analysis, Deep Learning and Spatial Database. Due to his great contributions to recommendation in social media, he was granted the Distinguished Doctor Degree Thesis Award of Peking University in 2014. Besides, he held the honors of outstanding graduate from Beijing provincial government of P.R. China. He was the winner of the National Scholarship from Ministry of Education of P.R. China in 2008 as well as the winner of National Graduate Scholarship from Ministry of Education of P.R. China in 2013. Dr. Yin has published over 30 related peer-reviewed publications in prestigious journals and conferences of the database, data mining and information retrieval fields, including SIGMOD, VLDB, KDD, ICDE, ACM Multimedia, CIKM, TOIS (ACM Transactions on Information Systems), TKDD (ACM Transactions on Knowledge Discovery from Data), TIST (ACM Transactions on Intelligent Systems and Technology) and World Wide Web. He has served in the Technical Program Committee of various international conferences including IEEE International Conference on Data Science in Cyberspace 2016, WISE 2016&2015, APWEB 2016&2015, DEXA 2016&2015, WAIM 2016&2015. He has also serve as invited reviewers for several prestigious journals such as VLDB Journal, IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on the Web (TWeb), IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing (TCC), Pervasive and Mobile Computing (PMC), New Review of Hypermedia and Multimedia, Frontiers of Computer Science (FCS), Journal of Image and Vision Computing, Knowledge-Based Systems, New Review ofHypermedia and Multimedia.
Schlagwörter
Recommander system, User behavior modeling, Social media mining, Location-based service, Query processing algorithm, Data sparsity, Spatial database, Cold-start problem
Thema-Inhalt
UNF - Data Mining UYQE - Wissensbasierte Systeme, Expertensysteme UNH - Informationsrückgewinnung, Information Retrieval UND - Data Warehousing UX - Angewandte Informatik UN - Datenbanken
Höhe
235 mm
Breite
15.5 cm

Hersteller: Springer Nature Customer Service Center GmbH, Europaplatz 3, Heidelberg, Deutschland, 69115, ProductSafety@springernature.com

Warnhinweise und Sicherheitsinformationen

Informationen nach EU Data Act

-.-
Leider noch keine Bewertungen
Leider noch keine Bewertungen
Sicher bei rebuy kaufen
Schreib die erste Bewertung für dieses Produkt!
Wenn du eine Bewertung für dieses Produkt schreibst, hilfst du allen Kund:innen, die noch überlegen, ob sie das Produkt kaufen wollen. Vielen Dank, dass du mitmachst!
Sicher bei rebuy kaufen