==== 2nd Call for Challenge: 2nd Linked Open Data-enabled Recommender Systems Challenge====

** apologies for cross-posting **

==== 2nd Call for Challenge: 2nd Linked Open Data-enabled Recommender 
Systems Challenge====

Challenge Website: 
http://sisinflab.poliba.it/events/lod-recsys-challenge-2015

MOTIVATION AND OBJECTIVES
People generally need more and more advanced tools that go beyond those 
implementing the canonical search paradigm for seeking relevant 
information. A new search paradigm is emerging, where the user's 
perspective is completely reversed: from finding to being found.
Recommender systems may help to support this new perspective, because 
they have the effect of pushing relevant objects, selected from a large 
space of possible options, to potentially interested users. To achieve 
this result, recommendation techniques generally rely on data referring 
to three kinds of objects: users, items, and their relations.
Recent developments in the Semantic Web community offer novel strategies 
to represent data about users, items and their relations 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.
More and more semantic data are published following the Linked Data 
principles, that enable links to be set up between objects in different 
data sources, by connecting information in a single global data space: 
the Web of Data. Today, the 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, ...). These 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 primary goal of this challenge is twofold. On the one hand, we want 
to enforce the link between the Semantic Web and the Recommender Systems 
communities. On the other hand, we  aim to showcase how Linked Open Data 
and semantic technologies can boost the creation of a new breed of 
knowledge-enabled and content-based recommender systems.

TARGET AUDIENCE
The target audience is all of those communities, both academic and 
industrial, which are interested in personalized information access with 
a particular emphasis on Linked Open Data.
During the last ACM RecSys conference the vast majority of participants 
were from industry. This is evidence of the actual interest of 
recommender systems for industrial applications ready to be released in 
the market.

DATA
We collected data from Facebook profiles about three distinct domains: 
movies, books and musical artists. After a process of anonymization we 
then reconciled the data with DBpedia entities. This data will be made 
available to train the recommendation algorithms. In order to emphasize 
the usefulness of content-based data, only "cold users" will be 
available in the dataset.

TASKS
- Task 1: Top-N recommendations from unary user feedback -
This task deals with the top-N recommendation problem, in which a system 
is requested to find and recommend a limited set of N items that best 
match a user profile, instead of correctly predicting the ratings for 
all available items. In order to favour the proposal of content-based, 
LOD-enabled recommendation approaches, and limit the use of 
collaborative filtering approaches, this task aims to generate ranked 
lists of items for which only unary feedback information (LIKE) is 
provided. For this task, we will concentrate only on the movie domain.

- Task 2: Diversity within recommended item sets -
A very interesting aspect of content-based recommender systems, and also 
of LOD-enabled ones, is providing the opportunity to evaluate the 
diversity of recommended items in a straightforward manner. This is a 
very popular topic in content-based recommender systems, which usually 
suffer from over-specialization. In this task, the evaluation will be 
made by considering a combination of both accuracy of the recommendation 
list, and the diversity of items belonging within it. Focusing on 
recommending musical artists, we will consider diversity with respect to 
the <http://dbpedia.org/ontology/genre>property.

- Task 3: Cross-domain recommendation -
This task aims to address a cross-domain recommendation scenario in 
which user preferences and/or domain knowledge of a source domain are 
used to recommend items in a different target domain. This may 
correspond with the following use cases. The first refers to the well 
known cold-start problem, which hinders the recommendation generation 
due to the lack of sufficient information about users or items. In a 
cross-domain setting, a recommender may draw on information acquired 
from other domains to alleviate such problem, e.g. a user’s favourite 
movie genres may be derived from her favourite book genres. The second 
refers to the generation of personalized cross-selling or bundle 
recommendations for items from multiple domains, e.g. a movie 
accompanied by a music album similar to the soundtrack of the movie. 
These relations may not be extracted from rating correlations within a 
joined movie-music rating matrix.
In this task, we will request participants to exploit user preferences 
and domain knowledge about movies, in order to provide book 
recommendations.
Making this task highly challenging, we will provide the list of books 
available in the test set, but we will provide little information about 
the users’ book preferences. Thus, we encourage not (only) to use 
collaborative filtering strategies based on correlations between movie 
and book preferences, but to investigate approaches that exploit LOD 
relating both movies and books domains.

JUDGING AND PRIZES
After a first round of reviews, the Program Committee and the chairs 
will select a number of submissions that will have to satisfy the 
challenge’s requirements, and will have to be presented at the 
conference. Submissions accepted for presentation will receive 
constructive reviews from the Program Committee, and will be included in 
post-proceedings. All accepted submissions will have a slot in a poster 
session dedicated to the challenge. In addition, the winners will 
present their work in a special slot of the main program of ESWC’15, and 
will be invited to submit a chapter to a post-proceedings book published 
by Springer (Communications in Computer and Information Science series).

For each task we will select:
* the best performing tool, given to the paper which will get the 
highest score in the evaluation
* the most original approach, selected by the Challenge Program 
Committee with the reviewing process

HOW TO PARTICIPATE
We invite the potential participants to subscribe to our mailing list in 
order to be kept up to date with the latest news related to the challenge.
lod-recsys-challenge-2015@googlegroups.com

* Make your result submission
- Register your group using the registration web form available at 
http://dee020.poliba.it:8181/eswc2014lodrecsys/signup.html.
- Choose one or more tasks among Task1, Task2 and Task3 (see Tasks).
- Build your Recommendation System using the training data described in 
section Dataset.
- Evaluate your approach by submitting your results using the evaluation 
service as described in section Evaluation.
- Your final score will be the one computed with respect to the last 
result submission made before March 25, 2015, 23:59 CET.

* Submit your paper
The following information has to be provided:
- Abstract: no more than 200 words.
- Description: It should contain the details of the system, including 
why the system is innovative, how it uses Semantic Web, which features 
or functions the system provides, what design choices were made, and 
what lessons were learned. The description should also summarize how 
participants have addressed the evaluation tasks and the results 
evaluation. Papers must be submitted in PDF format, following the style 
of the Springer’s Lecture Notes in Computer Science (LNCS) series 
(http://www.springer.com/computer/lncs/lncs+authors), and not exceeding 
12 pages in length.

All submissions should be provided via EasyChair 
https://www.easychair.org/conferences/?conf=eswc2015-challenges

IMPORTANT DATES
* Wednesday, March 25, 2015, 23:59 CET: Paper and Results Submission due
* Thursday, April 16, 2015, 23:59 CET: Notification of acceptance and 
submission of task results
* May 31- June 4, 2015: The Challenge takes place at ESWC-15


CHALLENGE CHAIRS
* Iván Cantador – Universidad Autónoma de Madrid, Spain
* Tommaso Di Noia – Polytechnic University of Bari, Italy
* Vito Claudio Ostuni – Pandora Media, Inc. USA
* Matthew Rowe – University of Lancaster, UK

PROGRAM COMMITTEE
* Roi Blanco, Yahoo! Labs, Barcelona, Spain
* Pablo Castells, Universidad Autónoma de Madrid, Spain
* Miriam Fernández, The Knowledge Media Institute, The Open University, UK
* Ignacio Fernández-Tobías, Universidad Autónoma de Madrid, Spain
* Frank Hopfgartner, Technische Universität Berlin, Germany
* Julia Hoxha, Columbia University, USA
* Dietmar Jannach, TU Dortmund University, Germany
* Pasquale Lops, University of Bari Aldo Moro, Italy
* Valentina Maccatrozzo, VU University Amsterdam, The Netherlands
* Alexandre Passant, Clarity.fm, USA
* Mariano Rico, Universidad Politécnica de Madrid, Spain
* Giovanni Semeraro, University of Bari Aldo Moro, Italy
* Manolis Wallace, University of Peloponnese, Greece
* Markus Zanker, Alpen-Adria-Universitaet Klagenfurt, Austria

TECHNICAL CHAIR
* Paolo Tomeo, Polytechnic University of Bari, Italy

ESWC CHALLENGE COORDINATORS
* Elena Cabrio, INRIA Sophia-Antipolis Méditerranée, France
* Milan Stankovic, Sépage & Université Paris-Sorbonne, France

-- 
Thanks
Matthew

-- 
Thanks
Matthew