CAPS 2013 - Workshop on Context-Awareness and Personalization Systems (CAPS)

This workshop aims to capture the state-of-the-art by soliciting original novel contributions in reputation and recommender algorithms, context-aware personalization systems, and privacy and security in personalization systems.
Today, the quantity of available information grows rapidly, overwhelming consumers to discover useful information and filter out the irrelevant items. The explosive growth of the Internet has made this issue increasingly more serious. Thus, the user is confronted with a big challenge of finding the most relevant information or item in the short amount of time. Without some support, the process of filtering out irrelevant items and finally selecting the most appropriate one could be very difficult. Personalization systems are aimed at addressing this overload problem, suggesting to the users those items that meet their interests and preferences the best in a particular situation and context. Additionally, an increasing number of applications require personalization systems to make predictions without full knowledge of the problem they are trying to solve and/or the available data is incomplete, uncertain, inconsistent and/or intentionally-contaminated. Further, the importance and use of contextual information (such as location, time, etc.) in personalization systems are yet to be explored by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, data mining, and marketing.
The workshop invites original technical papers that have not been published and are not currently under review for publication elsewhere. It is aimed to identify open problems, relevant research directions, and opportunities for innovation in the personalization and reputation systems. The workshop seeks to stir further interest for these topics in the community, and stimulate the research and progress in this area. Suggested topics include, but are not limited to the following:
--Recommender and personalization systems
--Trust and reputation management algorithms
-- User modeling and user studies in reputation and recommender systems
-- Context modeling for personalization systems;
-- Context-aware user modeling for recommender systems;
-- Novel applications for context-aware personalization systems;
-- Evaluation metrics and studies for reputation and recommender systems
-- Evaluation of context-aware personalization systems.
--Privacy and security in personalization systems
-- Machine learning for trust, reputation, and recommendation systems