Abstract:
As the accessibility of numerous music
music recommender became
streaming services has been extended, the
more and more relevant. Many oi these streaming
services, such as Spotify, have their
advances in
own lecommendation system. Despite several
recommendation techniques, systems recommendations are usually
still not correct.
This paper provides
music data collection from
recommendation as a
an overview of the history and developments of
a high content. This thesis also describes the
backlog and the methodology ofmusic recommendations by
content options and content simulations pioviding detailed descriptions of sound
used in music content advisors. Many of the measuring options granted for our
own content
Objective and subjective analysis of the
researchers ' results that the music advice based
offer the most correct guidance.
enforced system further ensures the
on audio content alone does not
In order to make sure that advice is
recommendation is listed as factors, this thesis focuses
recommendation and describes certain
used in content-based recommender
discussed. The history and development of the
correctly defined as a problem and
♦
on the content-based music
key audio options and similarity measures
music are
recovery analytics
in Chapter Three,
is summarized shortly.
systems. The challenges related to
music data
management with the guideline for target content is discussed
An analysis of alternative recommendation techniques
