[ESWC 2015] Call for Challenge: 2nd Linked Open Data-enabled Recommender Systems Challenge

** apologies for cross-posting **

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

Challenge Website: http://sisinflab.poliba.it/events/lod-recsys-challenge-2015
Call Web page: http://2015.eswc-conferences.org/important-dates/call-RecSys

12th Extended Semantic Web Conference (ESWC) 2015
Dates: May 31 - June 4, 2015
Venue: Portoroz, Slovenia
Hashtag: #eswc2015
Feed: @eswc_conf
Site: http://2015.eswc-conferences.org


GENERAL CHAIR
- Fabien Gandon (Inria, Sophia Antipolis, France)


CHALLENGE COORDINATORS
- Elena Cabrio (Inria, Sophia Antipolis, France)
- Milan Stankovic (SEPAGE, Paris, France)


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


IMPORTANT DATES
- Tuesday, March 03, 2015, 23:59 CET: Paper Submission due
- Thursday, April 09, 2015, 23:59 CET: Notification of acceptance and
submission of task results
- May 31- June 4, 2015: The Challenge takes place at ESWC-15

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
perspective is completely reversed: from finding to being found.
Recommender systems may help to support this new perspective, since
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 entities: 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, which enable to set up links between entities 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: a sedimentary one (encyclopedic,
cultural, linguistic, and common-sense) and a real-time one (news,
data streams, etc.). 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 the above 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 for sure a witness of the
actual interest of recommender systems for industrial applications
ready to be released in the market.

DATASET
We collected data from Facebook profiles about personal preferences
(“likes”) for items in three domains: movies, books and music. After a
process of user anonymization, we reconciled the item data with
DBpedia entities. This data will be made available to train proposed
recommendation approaches. 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 predict the
ratings for all available items.
In order to favor the proposal of content-based, LOD-enabled
recommendation approaches, and limit the use of collaborative
filtering approaches, this task aims at generating ranked lists of
items for which only unary feedback (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
then of LOD-enabled ones is giving the possibility to evaluate the
diversity of recommended items in a straight way. 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 to it.
Focusing on recommending musical artists, we will consider diversity
with respect to the <http://dbpedia.org/ontology/genre> and
<http://purl.org/dc/terms/subject> properties.

- 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 favorite movie genres may be derived from her favorite
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
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. 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.
* Result evaluation: For the three tasks previously described, a
Web-accessible service will be provided in order to evaluate the
produced results. All the details about the format and the service URL
will be provided on the website.

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

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


PROGRAM COMMITTEE [TBC] -
* Pablo Castells, Universidad Autónoma de Madrid, Spain
* Oscar Corcho, Universidad Politécnica de Madrid, Spain
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Ignacio Fernández-Tobías, Universidad Autónoma de Madrid
* Frank Hopfgartner, Technische Universität Berlin, Germany
* Andreas Hotho, Universität Würzburg, Germany
* Dietmar Jannach, TU Dortmund University, Germany
* Pasquale Lops, University of Bari Aldo Moro, Italy
* Valentina Maccatrozzo, Delft University of Technology, The Netherlands
* Alexandre Passant, seevl.fm, Ireland
* Francesco Ricci, Free University of Bozen-Bolzano, Italy
* 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

Received on Friday, 13 February 2015 15:17:19 UTC