SeRSy 2013

2nd International Workshop on Recommender Systems meet Big Data & Semantic Technologies

co-located with ACM RecSys 2013

Scope

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

Topics of interest include (but are not limited to):

Program

14:00 - 14:15
Opening by the Workshop Co-Chairs
14:15 - 14:35
TRECT: A Hashtag Recommendation System for Twitter.
Mahmuda Rahman (Syracuse University), Qinyun Zhu (Syracuse University) Edmund Szu-Li Yu (Syracuse University ).
14:35 - 14:55
Using Linked Open Data to Improve Recommending on E-Commerce.
Ladislav Peska (Charles University Prague), Peter Vojtas (Charles University Prague)
14:55 - 15:15
An Ontology-Based Recommender System in E-Commerce.
Alfredo Cutolo (Poste Italiane, Italy), Giuseppe D'Aniello (Consorzio CRMPA, Italy), Francesco Orciuoli (Universitá di Salerno, Italy), Francesca Pettinati (Poste Italiane, Italy), Giuseppe Sansonetti (Universitá di Salerno, Italy), Catello Vitagliano (Poste Italiane, Italy)
15:15 - 15:30
Final Discussion and Closing
15:30
Coffee Break

Submission

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:

https://www.easychair.org/conferences/?conf=sersy2013

You need to open a personal account upon the first login, if you do not have one.

Important Dates

Organizing Committee

Program Committee

Contacts

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