=Paper=
{{Paper
|id=Vol-1245/paper0
|storemode=property
|title=Semantics-aware Content-based Recommender Systems
|pdfUrl=https://ceur-ws.org/Vol-1245/cbrecsys2014-keynote.pdf
|volume=Vol-1245
|dblpUrl=https://dblp.org/rec/conf/recsys/Lops14
}}
==Semantics-aware Content-based Recommender Systems==
<pdf width="1500px">https://ceur-ws.org/Vol-1245/cbrecsys2014-keynote.pdf</pdf>
<pre>
   Semantics-aware Content-based Recommender Systems

                                                                         Pasquale Lops
                                                             Department of Computer Science
                                                              University of Bari “Aldo Moro”
                                                                Via E. Orabona, 4, I70126
                                                                        Bari, Italy
                                                                        lops@di.uniba.it


ABSTRACT
Content-based recommender systems (CBRS) filter very large repos-
itories of items (books, news, music tracks, TV assets, web pages?)
by analyzing items previously rated by a user and building a model
of user interests, called user profile, based on the features of the
items rated by that user. The user profile is then exploited to rec-
ommend new potentially relevant items.
   CBRS usually use textual features to represent items and user
profiles, hence they inherit the classical problems of natural lan-
guage ambiguity. The ever increasing interest in semantic tech-
nologies and the availability of several open knowledge sources
have fueled recent progress in the field of CBRS. Novel research
works have introduced semantic techniques that shift a keyword-
based representation of items and user profiles to a concept-based
one.
   In this talk I will focus on the main problems of CBRS, such as
limited content analysis, and overspecialization, showing the cur-
rent research directions for overcoming them, including

     • top-down semantic approaches, based on the use of different
       open knowledge sources (ontologies, Wikipedia, DBpedia)
     • bottom-up semantic approaches, based on the distributional
       hypothesis, which states that "words that occur in the same
       contexts tend to have similar meanings"
     • cross-language recommender systems and algorithms for learn-
       ing multilingual content-based profiles
     • the generation of serendipitous recommendations




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CBRecSysand
published   2014, October 6,by
                copyrighted    2014,  Silicon Valley, CA, USA.
                                  its editors.
Copyright 2014
CBRecSys    2014,by  the author(s).
                  October   6, 2014, Silicon Valley, CA, USA.




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