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From:
Iván Cantador <[log in to unmask]>
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Iván Cantador <[log in to unmask]>
Date:
Tue, 4 Feb 2014 17:24:48 +0100
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============================================================

ESWC’14 Challenge: Linked Open Data-enabled Recommender Systems

 

Challenge Website: http://challenges.2014.eswc-conferences.org/RecSys 

 

Call Web page: http://2014.eswc-conferences.org/important-dates/call-RecSys


============================================================

 

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, 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 that fully understand the items they
deal with.

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, 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
create a link between the Semantic Web and the Recommender Systems
communities. On the other hand, we  aim to show how Linked Open Data (LOD)
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 the Semantic Web and the Recommender Systems
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 more than 60% 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.

 

TASKS

 

* Task 1: Rating prediction in cold-start situations

This task deals with the rating prediction problem, in which a system is
requested to estimate the value of unknown numeric scores (a.k.a. ratings)
that a target user would assign to available items, indicating whether she
likes or dislikes them.

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 predicting ratings in cold-start situations, that is,
predicting ratings for users who have a few past ratings, and predicting
ratings of items that have been rated by a few users.

The dataset to use in the task - DBbook - relates to the book domain. It
contains explicit numeric ratings assigned by users to books. For each book
we provide the corresponding DBpedia URI.

Participants will have to exploit the provided ratings as training sets, and
will have to estimate unknown ratings in a non-provided evaluation set.

Recommendation approaches will be evaluated on the evaluation set by means
of metrics that measure the differences between real and estimated ratings,
namely the Root Mean Square Error (RMSE).

 

* Task 2: Top-N recommendation from binary 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.

Similarly to Task 1, in order to favor 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 no graded ratings are available, but only binary ones. Also in this
case, the DBbook dataset is used.

In this task, the accuracy of recommendation approaches will be evaluated on
an evaluation set using the F-measure.

 

*  Task 3: Diversity

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 (F-measure) of the recommendation list and the diversity
(Intra-List Diversity) of items belonging to it. Also for this task, the
DBbook dataset is used.

Given the domain of books, diversity with respect to the two properties
http://dbpedia.org/ontology/author and http://purl.org/dc/terms/subject will
be considered.

 

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
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’14, and will be invited to submit a paper to a
dedicated Semantic Web Journal special issue.

 

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

 

An amount of 700 Euro has already been secured for the final prize. We are
currently working on securing further funding.

 

IMPORTANT DATES

* March 7, 2014, 23:59 CET: Result submission due

* March 14, 2014, 23:59 CET: Paper submission due

* April 9, 2014, 23:59 CET: Notification of acceptance

* May 27-29, 2014: The Challenge takes place at ESWC’14

 

CHALLENGE CHAIRS

* Tommaso Di Noia (Polytechnic University of Bari, IT)

* Iván Cantador  (Universidad Autónoma de Madrid, ES)

 

EVALUATION COORDINATOR

* Vito Claudio Ostuni (Polytechnic University of Bari, IT)

 

PROGRAM COMMITTEE (to be completed)

* Pablo Castells, Universidad Autonoma de Madrid, Spain

* Oscar Corcho, Universidad Politécnica de Madrid, Spain

* Marco de Gemmis, University of Bari Aldo Moro, Italy

* 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, VU University Amsterdam, The Netherlands

* Roberto Mirizzi, Polytechnic University of Bari, Italy

* Alexandre Passant, seevl.fm, Ireland

* Francesco Ricci, Free University of Bozen-Bolzano, Italy

* Giovanni Semeraro, University of Bari Aldo Moro, Italy

* David Vallet, NICTA, Australia

* Manolis Wallace, University of Peloponnese, Greece

* Markus Zanker, Alpen-Adria-Universitaet Klagenfurt, Austria

* Tao Ye, Pandora Internet Radio, USA

 

ESWC CHALLENGE COORDINATOR

* Milan Stankovic (Sépage & Université Paris-Sorbonne, FR)

 


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