Appel: ESWC 2014, Challenge Linked Open Data-enabled

Tue Feb 11 20:57:26 UTC 2014

Date: Mon, 10 Feb 2014 15:09:59 +0100 (CET)
From: speroni at cs.unibo.it
Message-Id: <20140210141019.5E85E119754 at vina.cines.fr>
X-url: http://challenges.2014.eswc-conferences.org/RecSys

** apologies for cross-posting **

= Second Call for 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 

11th Extended Semantic Web Conference (ESWC) 2014
Dates: May 25 - 29, 2014
Venue: Anissaras, Crete, Greece
Hashtag: #eswc2014
Feed: @eswc_conf
Site: http://2014.eswc-conferences.org
General Chair: Valentina Presutti (STLab, ISTC-CNR, IT)
Challenge Coordinator: Milan Stankovic (Sepage & Universite Paris-Sorbonne, FR)
Challenge Chairs:
- Tommaso Di Noia (Polytechnic University of Bari, IT)
- Ivan Cantador (Universidad Autonoma de Madrid, ES)

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.

DATASET

* DBbook dataset

This dataset relies on user data and preferences retrieved from the
Web. The books available in the dataset have been mapped to their
corresponding DBpedia URIs. The mapping contains 8170 DBpedia URIs.

These mappings can be used to extract semantic features from DBpedia or
other LOD repositories to be exploited by the recommendation approaches
proposed in the challenge.
The dataset is split in a training set and an evaluation set. In the
former, user ratings are provided to train a system while in the latter,
ratings have been removed, and they will be used in the eventual
evaluation step.

The mapping file is available at:
http://sisinflab.poliba.it/semanticweb/lod/recsys/2014challenge/DBbook_Items_DBpedia_mapping.tsv.zip

It contains a tab-separated values file where each line has the
following format: DBbook_ItemID \t name \t DBpedia_URI.

We suggest to extract a semantic descriptions for all the items present
in this mapping file by starting from the DBpedia URIs.

The training sets are available at:

* Task 1: http://sisinflab.poliba.it/semanticweb/lod/recsys/2014challenge/DBbook_train_ratings.zip 

The archive contains a tab-separated values file containing the training
data and a README describing its content. Each line in the file is
composed by: userID \t itemID \t rating. The ratings are in scale
0-5. The training set contains 75559 ratings. There are 6181 users and
6166 items which have been rated by at least one user.

* Task 2 and Task 3:  http://sisinflab.poliba.it/semanticweb/lod/recsys/2014challenge/DBbook_train_binary.zip 

The archive contains a tab-separated values file containing the training
data and a README describing its content. Each line in the file is
composed by: userID \t itemID \t rating. The ratings are in binary
scale. 1 means that the item is relevant for the user, 0 means
irrelevant. The training set contains 72372 ratings. There are 6181
users and 6733 items which have been rated by at least one user.

ADDITIONAL DATASETS

Although not used in the challenge, two additional rating datasets
linked to DBpedia are provided, namely the well known MovieLens10M
dataset and the Last.fm dataset published at HetRec'11 workshop.

http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/ 

We encourage participants to use these datasets for testing the
developed recommendation approaches on several 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 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.

Winners will be selected only for tasks with at least 3 participants. In
any case, all submissions will be reviewed and, if accepted, published
at ESWC post-proceedings.

HOW TO PARTICIPATE

1.  Make your result submission
* Register your group using the registration web form available at:
  http://193.204.59.20:8181/eswc2014lodrecsys/signup.html
* Choose one or more tasks among Task 1, Task 2 and Task 3
* Build your recommender system using the provided training data.
* Evaluate your approach by submitting your results using the evaluation
  service.
* Your final score will be the one computed with respect to the last
  result submission made before March 7, 2014, 23:59 CET.

2. 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. 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 5 pages in length.

All submissions should be provided via EasyChair: 

https://www.easychair.org/conferences/?conf=eswc2014-challenges 

MAILING LIST

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.

https://lists.sti2.org/mailman/listinfo/eswc2014-recsys-challenge 

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

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 Politecnica de Madrid, Spain
* Marco de Gemmis, University of Bari Aldo Moro, Italy
* Frank Hopfgartner, Technische Universitat Berlin, Germany
* Andreas Hotho, Universitat Wurzburg, 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