Re: Data Import Policy

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Den 12-05-2015 kl. 16:00 skrev Andre Wiethoff:
>> This should not be a relationship like we currently have relationships=
=2E
>> This is an entirely subjective piece of information and some people wi=
ll
>> consider two artists similar while others will not.
> Yes, therefore I did propose to not open it for public editing, but=20
> having it "computer generated" only (by whatever means, be it web=20
> crawling or clustering on large data sets).

But you proposed it to be stored as a relationship like the current
relationships in the MusicBrainz database=E2=80=A6 (read below)

> But in the end you agree that the result of such a web crawl/clustering=
=20
> algorithm/whatever should be stored in the database as final result (fo=
r=20
> speedier access of the results) - if implemented at all? But perhaps we=
=20
> should discuss at first whether the new data would be beneficial for th=
e=20
> users (or the database)...

In *a* db, sure, in *the* (MB) db, no. It is not objective data and it
would not be user generated. It would be far more reasonable to place it
in another (sub)project. See e.g., AcousticBrainz and CritiqueBrainz for
two MetaBrainz projects expanding on the MusicBrainz data without being
inserted directly into the MB site/data themselves. A
RecommendationBrainz or SimilarityBrainz (or, heck, maybe it could be
part of CritiqueBrainz?) would be a better fit for this.

(Also note that having it in a separate project does not mean it cannot
be used by/on MusicBrainz; e.g., CritiqueBrainz reviews are pulled in
for relevant MB release( group)s.)

>> There are already
>> projects (though I forget which, sorry) that group/cluster entities
>> based on relationships (which IIRC wasn't completely off), so it is
>> possible to do something like it with the data in MB already.
> I wonder which relationships have been used to group/cluster the=20
> entities? [=E2=80=A6]

IIRC, all the relationships. The more times two entities linked to each
other, the closer those two entities were. AFAIK, it's a fairly simple
heuristic, but given the amount of relationships in the MB db, it should
give reasonable results for most fairly well known artists.

>> There's
>> also AcousticBrainz which can be used to cluster entities based on the=

>> acoustic properties of their recordings.
> I don't think that clustering regarding the acoustic properties will=20
> bring any good results for now, I guess this will still take ten years =

> until there exist something that produces results matchable to a human =

> expert (or even advanced amateur)...

I wouldn't make it stand on its own, no. ABz is still very much in its
infancy and the tools and algorithms in Essentia are not yet up to par
with this massive 2+ million song dataset currently available in the ABz
database. However, ABz can give you ranges about whether a group does
mostly vocal or instrumental things, whether they're mostly high or low
BPM, whether they have a predominant mood, etc.

These aren't necessarily 100% accurate, but combining similarity on
these values with relationship clustering, I think it may be possible to
get some interesting results (e.g., two artists with a lot of
relationships connecting them that additionally does mostly acoustic,
instrumental happy+relaxed music are likely more similar than two
artists with no relationships connecting them and one doing mostly
instrumental and the other doing mostly vocal stuff).

When/if we get access to scrobbles, that's a third data source that can
be added to the mix, but I really do not think we need it to get started
on a similarity/recommendation engine.

--=20
Namast=C3=A9,
Frederik =E2=80=9CFreso=E2=80=9D S. Olesen <http://freso.dk/>
MB:   https://musicbrainz.org/user/Freso
Wiki: https://wiki.musicbrainz.org/User:Freso


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