Workshop on context-aware recommender systems
MetadataShow full item record
Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2019 workshop provides a venue for presenting and discussing approaches for next generation of CARS and application domains that may require a variety of dimensions of contexts and cope with its dynamic properties.
Showing items related by title, author, creator and subject.
Braunhofer M; Elahi M; Ricci F (2014)Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings ...
Braunhofer M; Elahi M; Ricci F (Springer, Cham, 2015)The new user problem is an important and challenging issue that Context-Aware Recommender Systems (CARSs) must deal with, especially in the early stage of their deployment. It occurs when a new user is added to the system ...
Gasparic M; Gurbanov T; Ricci F (IEEE Press, 2017)Integrated development environments (IDEs) are complex applications that integrate multiple tools for creating and manipulating software project artifacts. To improve users’ knowledge and the effectiveness of usage of the ...