[seqfan] Guided browsing of the OEIS based upon personal preferences?

Rick Shepherd rlshepherd2 at gmail.com
Mon Oct 19 17:51:05 CEST 2009

Hello, SeqFans,

Bear with me, I'm not really being off-topic (although this may be
interesting to some people strictly for music-related reasons).
This note will soon come back to the OEIS.  I'll also emphasize now that the
OEIS already has a Browse feature:
http://www.research.att.com/~njas/sequences/Sbrowse.html  (although I'm
having difficulty accessing the database at the moment)

Today I ran across an article describing a system called Pandora for
suggesting songs one may like based upon one's previous statements of
preferred songs.  Actually, my son had already told me about Pandora at
least twice -- but this is the first time I've seen a bit about the
nuts-and-bolts of how these suggestions are made.  Most of us have probably
experienced some apparently-similar system in some sphere where a product is
suggested because "other buyers of this product also bought these other
products" (e.g., Amazon and books/etc) or where the software (using cookies,
etc.) is clearly attempting to learn what we like (i.e., this isn't new).
Pandora, in contrast to some of these others, attempts to be more objective
and makes suggestions based upon straightforward characterizations of
technical elements of songs rather than "collaborative filtering"
(popularity or what everyone else is doing) -- but, of course, matters of
(other people's) taste and subjectivity cannot be completely eliminated

If one replaced "Pandora's music collection" with "the OEIS" and "song" with
"sequence" (and drew several similar parallels), this article could give
some food for thought about future directions for the OEIS.  This article
also touches upon what to add to the collection and how to decide that --
topics that have been recently discussed on this list for the OEIS.

If the OEIS currently "Contains 164537 sequences", *one day* it might
actually be so large that it's difficult to find that which you're really
seeking.  :^J)   Algorithms based upon sophisticated, dynamic saved
searches (and more) could help direct those who aren't looking to "commit
complete serendipity" on a given day (The latter I admit is often the mode I
enjoy but certainly not always.).

Here's the link, "The Song Decoders":
(published Oct. 14th, 2009)
I've found (in the USA) that sometimes it's necessary to be logged-in to
one's NY Times account (free registration) to access their articles -- and
sometimes not -- even for the same articles (it seems partly to be based
upon time of day).


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