Algorithm-SlopeOne
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# Output:
# \ {
# cuttlefish 0.25,
# nautilus 0.1,
# octopus 0.233333333333333
# }
=head1 DESCRIPTION
Perl implementation of the I<Weighted Slope One> rating-based collaborative filtering scheme.
=head1 ATTRIBUTES
=head2 diffs
Differential ratings matrix.
=head2 freqs
Ratings count matrix.
=head1 METHODS
=head2 clear
Reset the instance.
=head2 add($userprefs)
Update matrices with user preference data, accepts a HashRef or an ArrayRef of HashRefs:
$s->predict({ StarWars => 5, LOTR => 5, StarTrek => 3, Prometheus => 1 });
$s->predict({ StarWars => 3, StarTrek => 5, Prometheus => 4 });
$s->predict([
{ IronMan => 4, Avengers => 5, XMen => 3 },
{ XMen => 5, DarkKnight => 5, SpiderMan => 3 },
]);
=head2 predict($userprefs)
Recommend new items given known item ratings.
$s->predict({ StarWars => 5, LOTR => 5, Prometheus => 1 });
=for Pod::Coverage new
=head1 TODO
Implement I<Non-Weighted> and I<Bi-Polar Slope One> schemes.
=head1 REFERENCES
=over 4
=item *
L<Slope One|https://en.wikipedia.org/wiki/Slope_One> - Wikipedia article
=item *
L<Slope One Predictors for Online Rating-Based Collaborative Filtering|http://lemire.me/fr/abstracts/SDM2005.html> - original paper
=item *
L<Collaborative filtering made easy|http://www.serpentine.com/blog/2006/12/12/collaborative-filtering-made-easy/> - Python implementation by Bryan O'Sullivan (primary reference, test code)
=item *
L<github.com/ashleyw/Slope-One|https://github.com/ashleyw/Slope-One> - Ruby port of the above by Ashley Williams (used to borrow test code)
=item *
L<Programming Collective Intelligence book|http://shop.oreilly.com/product/9780596529321.do> by Toby Segaran
=item *
L<Data Sets by GroupLens Research|http://www.grouplens.org/node/12>
=back
=head1 AUTHOR
Stanislaw Pusep <stas@sysd.org>
=head1 COPYRIGHT AND LICENSE
This software is copyright (c) 2014 by Stanislaw Pusep.
This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.
=cut
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