Organizing Committee Workshop Co-Chairs August 2016 ToineBogers FabioGasparetti CasperPetersen MarcoTkalčič Institute of Sound and Vision Cataldo Musto Aalborg University Copenhagen Marijn Koolen
Netherlands
" Pasquale Lops University of Bari "Aldo Moro University of Bari "Aldo Moro" Giovanni Semeraro University of Bari "Aldo Moro" Program Committee Jon Atle Gulla NICTA Ludovico Boratto Politecnico de Milano Marco de Gemmis Politecnico di Bari Peter Dolog Norwegian University of Science and Technology Shlomo Berkovsky University of Cagliari Robin Burke DePaul University Iván Cantador Universidad Autónoma de Madrid Federica Cena Universita' degli Studi di Torino Paolo Cremonesi University of Bari Ernesto William De Luca Potsdam University of Applied Sciences Tommaso Di Noia Aalborg University Juan F. Huete Roma Tre University Cristina Gena Universita' degli Studi di Torino Frank Hopfgartner University of Glasgow 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 Shaghayegh Sahebi University of Copenhagen University of Pittsburgh Alan Said University of Skövde Free University of Bozen-Bolzano Bei Yu Syracuse University Organizing Committee Workshop Co-Chairs August 2016 DD27D1A63EF1875E9C9A499C7040673A GROBID - A machine learning software for extracting information from scholarly documents
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 contentbased 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.

Table of Contents Invited presentations From Reviews to RecommendationsBarry Smyth1Context v. Content: The Role of Semantic and Social Knowledge inContext-aware RecommendationBamshad Mobasher2Accepted papersCombining Content-based and Collaborative Filtering for Personalized Sports NewsRecommendationsPhilip Lenhart, Daniel Herzog3News Article Position Recommendation Based on The Analysis of Article'sContent -Time MattersParisa Lak, Ceni Babaoglu, Ayse Basar Bener, Pawel Pralat11Using Visual Features and Latent Factors for Movie RecommendationYashar Deldjoo, Mehdi Elahi, Paolo Cremonesi15Recommending Items with Conditions Enhancing User Experiences Based onSentiment Analysis of ReviewsKonstantin Bauman, Bing Liu, Alexander Tuzhilin19
RDF Graph Embeddings for Content-based Recommender SystemsJessica Rosati, Petar Ristoski, Tommaso Di Noia, Renato De Leone, Heiko Paulheim23ReDyAl: A Dynamic Recommendation Algorithm based on Linked DataIacopo Vagliano, Cristhian Figueroa, Oscar Rodríguez Rocha, Marco Torchiano,Catherine Faron-Zucker, Maurizio Morisio31Quote Recommendation for Dialogs and WritingsYeonchan Ahn, Hanbit Lee, Heesik Jeon, Seungdo Ha, Sang-Goo Lee39Learning-to-
Rank in Research Paper CBF Recommendation: Leveraging Irrelevant PapersAnas 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

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