The 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
In conjunction with ACM KDD 2012, August 12, 2012, Beijing, China

In the field of Web and social network mining, more and more learning tasks can easily acquire multiple data sets from various domains. For example, a modern search engine system often conducts ranking learning tasks in various domains with different languages (e.g., English text search, Spanish text search, etc.), or different verticals/topics (e.g., news search, product search, etc.); and recently recommendation systems start to leverage multiple types of user data from different domains, such as user browsing history data, user shopping record data, and user social network data. At the same time, the need for knowledge transfer is increasingly evident as many new datasets, or parts of data, are only very sparsely annotated.

Different from traditional single-domain learning problems based on the assumption that training and test data are drawn from identical distribution, cross domain learning problems are built on multiple domain data that may have different degrees of relatedness to target tasks, offering an opportunity to help one another. To better leverage multiple domain data, mining and transferring of shared knowledge across multiple domains is likely to become a crucial step in Web and social network mining in the future.

Topics of Interest
Representative issues to be addressed include but are not limited to:
1. Cross domain ranking
2. Cross domain recommendation
3. Cross domain social network analysis
4. Cross domain natural language processing
5. Cross domain learning on structure data
6. Cross domain learning on stream data
7. Cross domain learning on heterogeneous data

Paper Submission
Papers should be no more than 8 pages in length. The format should be the same as KDD main track format.
All submissions must be in PDG format and must no exceed 10MB in size.
Top quality papers will be invited to IEEE Inteligent System Special Issue.