1st Workshop on Semantic Personalized Information Management
Search engines have become an essential tool for the majority of users for finding information in the huge amount of documents contained in the Web. Even though, for most ad-hoc search tasks, they already provide a satisfying performance, certain fundamental properties still leave room for improvement. For example, if users perform general questions, they get frequently lost in navigating the huge amount of documents returned and typically stop their search after scanning a couple of result pages. Basically, results are ranked based on word frequencies and link structures, but other factors, such as sponsored links and ranking algorithms, are also taken into account.
Standard search engines do not consider semantic information that can help in recognizing the relevance of a document with respect to the meaning of a query, so that users have to analyze every document and decide which documents are relevant with respect to the meaning implied in their search. Therefore, they also struggle for matching the individualized information needs of a user.
Since users are different, and want to access information according to their experience and knowledge, different techniques for constructing user models, analyzing user profiles and deriving information about a user for the adaptation of content have been proposed. An emerging approach is to use Semantic Web and Web 2.0 technologies to model information about users.
The workshop aims at improving the exchange of ideas between the different communities involved in the research on personalized information management. The workshop focuses especially on researchers that are working on Web 2.0 and Web 3.0, computational linguistics, machine learning, user modelling, and their combination.