I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Learning to Advertise for Organic Traffic Maximization in E..:
, In:
Proceedings of the 28th ACM International Conference on Information and Knowledge Management
,
Chen, Dagui
;
Jin, Junqi
;
Zhang, Weinan
... - p. 2527-2535 , 2019
Link:
https://dl.acm.org/doi/10.1145/3357384.3357819
RT T1 <cTitle>Proceedings of the 28th ACM International Conference on Information and Knowledge Management</cTitle>: T1 <Title>Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3357819&Exemplar=1&LAN=DE A1 Chen, Dagui A1 Jin, Junqi A1 Zhang, Weinan A1 Pan, Fei A1 Niu, Lvyin A1 Yu, Chuan A1 Wang, Jun A1 Li, Han A1 Xu, Jian A1 Gai, Kun PB ACM YR 2019 K1 e-commerce product feeds K1 interplay between advertisement and recommendation K1 online advertising K1 personalized recommendation K1 Information systems K1 Information systems applications K1 Computational advertising K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Reinforcement learning SP 2527 OP 2535 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3357384.3357819 DO https://dl.acm.org/doi/10.1145/3357384.3357819 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
