As part of trying to get a better grip on recommenders, I have started a
simple interactive visualization that begins with the raw data of user-item
interactions and goes all the way to being able to twiddle the interactions
in a test user vector to see the impact on recommended items. This is for
simple "user interacted with an item" case rather than numerical
preferences for items. The goal is to show the intermediate pieces and how
they fit together via popup text on mouseovers and dynamic highlighting of
the related pieces. I am of course interested in feedback as I keep
tweaking on it - not sure I got all the terminology quite right yet, for
example, and might have missed some other things I need to know about.
Note that this material is covered in Chapter 6.2 in MIA in the discussion
on distributed recommenders.
It's on googledrive here (very much a work-in-progress):
https://googledrive.com/host/0B2GQktu-wcTiWHRwZFJacjlqODA/
(apologies to small resolution screens)
This is based only on the co-occurrence matrix, rather than including the
other similarity measures, although in working through this, it seems that
the other ones can just be interpreted as having alternative definitions of
what "*" means in matrix multiplication of A^T*A, where A is the user-item
matrix... and as an aside to me begs the interesting question of [purely
hypotheticall?] situations where LLR and co-occurrence are at odds with
each other in making recommendations, as co-occurrence seems to be just
using the "k11" term that is part of the LLR calculation.
My goal (at the moment at least) is to eventually continue this for the
solr-recommender project that started as few weeks ago, where we have the
additional cross-matrix, as well as a kind of regrouping of pieces for solr.
simple interactive visualization that begins with the raw data of user-item
interactions and goes all the way to being able to twiddle the interactions
in a test user vector to see the impact on recommended items. This is for
simple "user interacted with an item" case rather than numerical
preferences for items. The goal is to show the intermediate pieces and how
they fit together via popup text on mouseovers and dynamic highlighting of
the related pieces. I am of course interested in feedback as I keep
tweaking on it - not sure I got all the terminology quite right yet, for
example, and might have missed some other things I need to know about.
Note that this material is covered in Chapter 6.2 in MIA in the discussion
on distributed recommenders.
It's on googledrive here (very much a work-in-progress):
https://googledrive.com/host/0B2GQktu-wcTiWHRwZFJacjlqODA/
(apologies to small resolution screens)
This is based only on the co-occurrence matrix, rather than including the
other similarity measures, although in working through this, it seems that
the other ones can just be interpreted as having alternative definitions of
what "*" means in matrix multiplication of A^T*A, where A is the user-item
matrix... and as an aside to me begs the interesting question of [purely
hypotheticall?] situations where LLR and co-occurrence are at odds with
each other in making recommendations, as co-occurrence seems to be just
using the "k11" term that is part of the LLR calculation.
My goal (at the moment at least) is to eventually continue this for the
solr-recommender project that started as few weeks ago, where we have the
additional cross-matrix, as well as a kind of regrouping of pieces for solr.