RSBD 2019 will focus on technologies and solutions related, but not limited to:<br>– Large–scale parallelisation and distributed processing techniques to speed up the offline training of complex recommender models;<br>– Scalable hash and indexing techniques to speed up the online recommendation and reduce the storage cost;<br>– Machine learning techniques that effectively extract and fuse heterogeneous and multi– modal content features to improve recommendations;<br>– Context–aware recommendation systems incorporating various contextual information such as location–based recommendation and social recommendation;<br>– Incremental recommendation solutions and online learning models to deal with continuous updates, especially real time streaming data for recommendations;<br>– Active learning techniques to acquire high–quality and informative user feedback data;<br>– Robust recommender models that are resistant to spam reviews and ratings;<br>– Spam detection techniques to discover the malicious attackers and spam reviews or ratings;<br>– Data cleaning techniques to improve the quality of user generated behaviour and content data.<br>For more information regarding important dates and submission formats, please refer to our official webpage.<br><div><br></div>
Abbrevation
IDCS
City
Napoli
Country
Italy
Deadline Paper
Start Date
End Date
Abstract