[loginf] [CFP] SeRSy at ACM RecSys 2013 - 2nd International Workshop on Recommender Systems meet Big Data & Semantic Technologies
lukasiew at gmail.com
Mon Jul 8 13:24:20 UTC 2013
Apologies for possible multiple posts
CALL FOR PAPERS
2nd International Workshop on
Recommender Systems meet Big Data & Semantic Technologies - SeRSy 2013
in conjunction with RecSys 2013, Hong Kong, October 12-16, 2013
Recommendation techniques generally rely on data referring to three kinds of objects: users, items and their relations. The widespread success of Semantic Web techniques, creating a Web of
interoperable and machine readable data, offers novel strategies to represent data that might improve the current state of the art of recommender systems, in order to move towards a new generation of
recommender systems which fully understand the items they deal with.
Indeed, more and more semantic data are published following the Linked Data principles, that enable to set up links between objects in different data sources, by connecting information in a single
global data space - the Web of Data. Today, Web of Data includes different types of knowledge represented in a homogeneous form - sedimentary one (encyclopedic, cultural, linguistic, common-sense and
real-time one (news, data streams. This data might be useful to interlink diverse information about users, items, and their relations and implement reasoning mechanisms that can support and improve
the recommendation process.
The challenge is to investigate whether and how this large amount of wide-coverage and linked semantic knowledge can significantly improve complex search and filtering tasks that cannot be solved
merely through a straightforward matching of queries (or user profiles) and items. Such tasks involve finding information from large document collections, categorizing and understanding that
information, and producing some output, such as an actionable decision. Examples of such tasks include understanding a health problem in order to make a medical decision, or simply deciding which
laptop to buy. Recommender systems support users exactly in those complex tasks.
Topics of interest include (but are not limited to):
- Recommendation approaches exploiting Big Data and Semantic technologies
- Linked Data for Recommender Systems
- Ontology-based recommendation algorithms
- Reasoning with Big Data
- Discovery of relevant Linked Data sources for recommendation algorithms
- Linking, aggregating, intertwining and mining Linked Data for recommender systems
- Linked Data in new Recommender Systems architectures
- Semantic technologies for Cross-lingual and cross-domain recommender systems
- Semantic technologies for improving transparency and explanations
- Big datasets for the evaluation
- Evaluation methodologies for real time personalization in big datasets
- Semantic technologies for improving novelty, diversity and serendipity
We welcome work at all stages of development: papers can describe applied systems, empirical results or theoretically grounded positions.
Accepted papers will be published as CEUR workshop proceedings (http://ceur-ws.org).
Based on the quality of accepted papers we are planning to schedule a special issue of a top-level journal in 2013.
* Full papers (5-6 pages)
* Short papers (2-3 pages)
* Demos (1-2 pages for description)
Papers should be formatted according to the general RecSys2013 submission guidelines. Accepted format is PDF.
Please submit your paper via EasyChair at the following URL:
You need to open a personal account upon the first login, if you do not have one.
Submission due: July 22, 2013
Author notification: August 23, 2013
Camera-ready version due: TBA
Workshop date: TBA
Marco de Gemmis - University of Bari Aldo Moro, Italy
Tommaso Di Noia - Politecnico of Bari, Italy
Ora Lassila - Nokia Research Center, Cambridge, U.S.
Pasquale Lops - University of Bari Aldo Moro, Italy
Thomas Lukasiewicz - University of Oxford, UK
Giovanni Semeraro - University of Bari Aldo Moro, Italy
e-mail: sersy2013 at gmail.com
Web page: http://sisinflab.poliba.it/sersy2013/
More information about the loginf