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. 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.
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 Organizing Committee
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"
From Reviews to Recommendations Barry Smyth Context v. Content: The Role of Semantic and Social Knowledge in Context-aware Recommendation Bamshad Mobasher
Combining Content-based and Collaborative Filtering for Personalized Sports News Recommendations Philip Lenhart, Daniel Herzog News Article Position Recommendation Based on The Analysis of Article's Content - Time Matters Parisa Lak, Ceni Babaoglu, Ayse Basar Bener, Pawel Pralat Using Visual Features and Latent Factors for Movie Recommendation Yashar Deldjoo, Mehdi Elahi, Paolo Cremonesi Recommending Items with Conditions Enhancing User Experiences Based on Sentiment Analysis of Reviews Konstantin Bauman, Bing Liu, Alexander Tuzhilin RDF Graph Embeddings for Content-based Recommender Systems Jessica Rosati, Petar Ristoski, Tommaso Di Noia, Renato De Leone, Heiko Paulheim ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data Iacopo Vagliano, Cristhian Figueroa, Oscar Rodríguez Rocha, Marco Torchiano, Catherine Faron-Zucker, Maurizio Morisio Quote Recommendation for Dialogs and Writings Yeonchan Ahn, Hanbit Lee, Heesik Jeon, Seungdo Ha, Sang-Goo Lee 1 2 3 11 15 19 23 31 39 43