id: 05803554
dt: a
an: 05803554
au: Mao, Jianchang
ti: Scientific challenges in contextual advertising.
so: Yu, Jian (ed.) et al., Rough set and knowledge technology. 5th
    international conference, RSKT 2010, Beijing, China, October 15‒17,
    2010. Proceedings. Berlin: Springer (ISBN 978-3-642-16247-3/pbk).
    Lecture Notes in Computer Science 6401. Lecture Notes in Artificial
    Intelligence, 2 (2010).
py: 2010
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-16248-0_2
ab: Summary: Online advertising has been fueling the rapid growth of the Web
    that offers a plethora of free web services, ranging from search,
    email, news, sports, finance, and video, to various social network
    services. Such free services have accelerated the shift in people’s
    media time spend from offline to online. As a result, advertisers are
    spending more and more advertising budget online. This phenomenon is a
    powerful ecosystem play of users, publishers, advertisers, and ad
    networks. The rapid growth of online advertising has created enormous
    opportunities as well as technical challenges that demand computational
    intelligence. Computational Advertising has emerged as a new
    interdisciplinary field that studies the dynamics of the advertising
    ecosystem to solve challenging problems that rise in online
    advertising. In this talk, I will provide a brief introduction to
    various forms of online advertising, including search advertising,
    contextual advertising, guaranteed and non-guaranteed display
    advertising. Then I will focus on the problem of contextual
    advertising, which is to find the best matching ads from a large ad
    inventory to a user in a given context (e.g., page view) to optimize
    the utilities of the participants in the ecosystem under certain
    business constraints (blocking, targeting, etc). I will present a
    problem formulation and describe scientific challenges in several key
    technical areas involved in solving this problem, including user
    understanding, semantic analysis of page content, user response
    prediction, online learning, ranking, and yield optimization.
rv: