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 provided 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 was a big success with over 60 attendees and 16 submissions.
     For the second edition, CBRecSys 2015, we once again issued a Call for Papers asking for sub-
missions of novel research papers (both long and short) 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 rec-
ommendation, semantic recommendation, content-based recommendation to alleviate cold-start
problems, as well as serendipity, diversity and cross-domain recommendation.
     Each submission was received by three members of the program committee consisting of experts
in the field of recommender systems and information retrieval. We selected 6 long papers and 2
short papers from the 12 submissions for presentation at the workshop. We are also happy to have
professor Frank Hopfgartner of the University of Glasgow give a keynote presentation on capturing
user interests for content-based recommendation.
     We thank all PC members, our keynote speaker as well as authors of accepted papers for mak-
ing CBRecSys 2015 possible. We hope you will enjoy the workshop!


   Toine Bogers, Marijn Koolen, Iván Cantador




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