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https://ceur-ws.org/Vol-1673/preface.pdf
Proceedings of the
CBRecSys 2016
3rd Workshop on New Trends in
Content-based Recommender Systems
September 16, 2016
In conjunction with the
10th ACM Conference on Recommender Systems
Boston, MA, USA
Edited by
Toine Bogers, Pasquale Lops, Marijn Koolen,
Cataldo Musto, Giovanni Semeraro
Copyright © 2016 for the individual papers by the papers’ authors. Copying permitted for private and
academic purposes. This volume is published and copyrighted by its editors.
Preface
While content-based recommendation has been applied successfully in many different domains, it has not
seen the same level of attention as collaborative filtering techniques have. In recent years, competitions like
the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on advances in collaborative
filtering and how to utilize ratings and usage data. However, there are many domains where content and
metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains,
such as movies, the relationship between content and usage data has seen thorough investigation already, but
for many other domains, such as books, news, scientific articles, and Web pages we do not know if and how
these data sources should be combined to provide the best recommendation performance.
The CBRecSys workshop series aims to address this by providing a dedicated venue for papers dedicated to all
aspects of content-based recommendation. The first edition in Silicon Valley in 2014, and the second one in
Vienna were a big success.
For the third edition, CBRecSys 2016, we once again issued a call for papers asking for submissions of novel
research papers addressing recommendation in domains where textual content is abundant (e.g., books, news,
scientific articles, jobs, educational resources, Web pages, etc.) as well as dedicated comparisons of content-
based techniques with collaborative filtering in different domains. Other relevant topics included opinion
mining for text/book recommendation, semantic recommendation, content-based recommendation to
alleviate cold-start problems, deep learning for content representation, as well as serendipity, diversity and
cross-domain recommendation.
Each submission was rewiewed by three members of the program committee consisting of experts in the field
of recommender systems and information retrieval. We selected 9 papers from the 14 submissions for
presentation at the workshop.
We are also happy to have Prof. Barry Smyth of the University College Dublin and Prof. Bamshad Mobasher of
the DePaul Univesity as keynote speakers.
We thank all PC members, our keynote speakers, as well as authors of accepted papers for making CBRecSys
2016 possible. We hope you will enjoy the workshop!
Toine Bogers, Pasquale Lops, Marijn Koolen, Cataldo Musto, Giovanni Semeraro
August 2016
Organizing Committee
Workshop Co-Chairs
Toine Bogers, Aalborg University Copenhagen
Marijn Koolen, Netherlands Institute of Sound and Vision
Cataldo Musto, University of Bari "Aldo Moro"
Pasquale Lops, University of Bari "Aldo Moro"
Giovanni Semeraro, University of Bari "Aldo Moro"
Program Committee
Jon Atle Gulla, Norwegian University of Science and Technology
Shlomo Berkovsky, NICTA
Ludovico Boratto, University of Cagliari
Robin Burke, DePaul University
Iván Cantador, Universidad Autónoma de Madrid
Federica Cena, Universita' degli Studi di Torino
Paolo Cremonesi, Politecnico de Milano
Marco de Gemmis, University of Bari
Ernesto William De Luca, Potsdam University of Applied Sciences
Tommaso Di Noia, Politecnico di Bari
Peter Dolog, Aalborg University
Fabio Gasparetti, Roma Tre University
Cristina Gena, Universita' degli Studi di Torino
Frank Hopfgartner, University of Glasgow
Juan F. Huete, Universidad de Granada
Jaap Kamps, University of Amsterdam
Silvia Likavec, Universita' degli Studi di Torino
Babak Loni, Delft University of Technology
Fedelucio Narducci, University of Bari
Casper Petersen, University of Copenhagen
Shaghayegh Sahebi, University of Pittsburgh
Alan Said, University of Skövde
Marco Tkalčič, Free University of Bozen-Bolzano
Bei Yu, Syracuse University
Table of Contents
Invited presentations
From Reviews to Recommendations
Barry Smyth 1
Context v. Content: The Role of Semantic and Social Knowledge in
Context-aware Recommendation
Bamshad Mobasher 2
Accepted papers
Combining Content-based and Collaborative Filtering for Personalized Sports News
Recommendations
Philip Lenhart, Daniel Herzog 3
News Article Position Recommendation Based on The Analysis of Article's
Content - Time Matters
Parisa Lak, Ceni Babaoglu, Ayse Basar Bener, Pawel Pralat 11
Using Visual Features and Latent Factors for Movie Recommendation
Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi 15
Recommending Items with Conditions Enhancing User Experiences Based on
Sentiment Analysis of Reviews
Konstantin Bauman, Bing Liu, Alexander Tuzhilin 19
RDF Graph Embeddings for Content-based Recommender Systems
Jessica Rosati, Petar Ristoski, Tommaso Di Noia, Renato De Leone, Heiko Paulheim 23
ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data
Iacopo Vagliano, Cristhian Figueroa, Oscar Rodríguez Rocha, Marco Torchiano,
Catherine Faron-Zucker, Maurizio Morisio 31
Quote Recommendation for Dialogs and Writings
Yeonchan Ahn, Hanbit Lee, Heesik Jeon, Seungdo Ha, Sang-Goo Lee 39
Learning-to-Rank in Research Paper CBF Recommendation: Leveraging
Irrelevant Papers
Anas Alzoghbi, Victor A. Arrascue Ayala, Peter M. Fischer, Georg Lausen 43
Recurrent Neural Networks for Customer Purchase Prediction on Twitter
Mandy Korpusik, Shigeyuki Sakaki, Francine Chen, Yan-Ying Chen 47