CAUMAP 2015 - First Workshop on Computational Advertising User Modeling, Adaptation and Personalization

The Computational Advertising Workshop aims to investigate and add to this emerging, multi-faceted research area where concepts from domains such as machine learning, information retrieval, economics, and data mining are used to find relevant and topical advertisements to show to a user as they consume content online. Typically, this entails intelligently matching advertising creative with an audience using a wide variety of behavioural, demographic and user-generated data, in addition to incorporating additional aggregated data sources to the decision making process.
The purpose of this workshops is to bring both researchers and applied researchers in the area of computational advertising so that they can discuss emerging trends, current state of the art approaches and to help further both practical implications and the wider community. A large part of computational advertising is how to represent a user when identifying or pairing them with ads.
GOALS
1. Bring together researchers and professionals working in the field of Computational Advertising.
2. Explore the future direction of the advertising industry from a user modeling, targeting and personalisation perspective and create new connections between the research community and industry.
3. Produce contributions that allow the research community to create new challenging results and real world applications in the field of computational advertising.
TOPICS
Relevant topics include (but are not limited to):
Audience segmentation
-Unsupervised Machine Learning
-Supervised Machine Learning
Machine Learning For Ranking
-Click-through rate prediction
-Advertiser behaviour prediction
-Ad Relevance
Data mining for internet advertising
-Recommender systems
-User behaviour log analysis
-Advertiser behaviour modelling and user behavioural modelling
Real-time bidding and market prediction
Fraud detection
Dynamic creative optimisation & Ad personalisation
Context-aware ad recommendation
Real-world deployments
Audience inference, i.e. utilising data from a subset of users so as to accurately infer what audience segment new users are likely to belong.
SUBMISSION INSTRUCTIONS
Submissions of high quality papers describing research results are solicited. Your contribution should contain original content and should be written in English. Submitted papers will be refereed for quality, correctness, originality, and relevance (to this workshop) by members of the program committee (see below). The submission requirements for this workshop are in line with standard UMAP formatting guidelines. We request potential submitters to adhere to Springer’s LNCS format. We would solicit both long and short papers. Long paper are to represent original mature research and can be of length 6 pages. Short papers are to represent early/promising research, demos or industrial case studies and can be of length 4 pages. Submissions must be made in PDF format and will be submitted through a submission system such as EasyChair or Microsoft’s CMT system. Selection of papers will be based on peer review by the larger program committee. It will be a single blind review and as such authors should include names and affiliations on submissions.
COMMITTEES
Organising committee:
Steven Bourke, Schibsted, Barcelona, Spain
John Hannon, Boxfish, Palo Alto, USA
Simon O’Regan, Independent News & Media, Dublin, Ireland
Program committee:
Brendan Kitts, Adap.tv
CONTACT
Please, visit the website: http://umap2015.com. For further enquires please contact chairs at soregan AT independent-digital.com || john-AT-boxfish.com || steven.bourk-AT-schibsted.com