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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Shopping cart Recommender (was Item-set Recommender)
Date Sun, 27 Apr 2014 21:23:06 GMT
Yeah... this Amazon hack is just a way to do multi-modal recommendations
without actually having a framework capable of multi-modal operation.  It
is similar to taking a rating as equivalent to a fraction of a purchase.
 Neither has great standing and neither is necessary if you segregate the
action streams and use multi-modal methods.



On Sun, Apr 27, 2014 at 4:58 PM, Pat Ferrel <pat@occamsmachete.com> wrote:

> The literature has examples of building rules from item-sets, which seems
> pretty archaic. Also Amazon did a paper (2003?) on using individual items
> from the user’s cart then getting similar items and summing the weights to
> get ordering. Also seems wrong since the actions/user intent doesn’t really
> match. Notice that the method below does not make use of the userID, it is
> specific to an item-set ID so the user intent is narrowed. In other words
> it is not measuring taste (a long lived user trait)
>
> Was wondering if anyone has used the method below. I don’t have data for
> shopping carts at the present. The last time I did, we used the Amazon
> method but it always seemed wrong. The one good thing about it is you have
> purchase data very early on but may not have enough shopping carts for some
> time and if you don’t have enough traffic you may never get timely enough
> carts to make this work. In other words the catalog may turn over too
> quickly.
>
>
> On Apr 27, 2014, at 3:37 AM, Ted Dunning <ted.dunning@gmail.com> wrote:
>
> In general, any action that can be detected in a user history can be an
> item (column) in the user history matrix.  If you find that there are
> item-sets that seem to occur together, then appearance of the entire
> item-set can be a reasonable feature to be assigned a column.  Somewhat
> more plausible is that you start to offer small packages of multiple items
> in a single order and you count browsing, interacting and buying these
> packages as different actions to be recorded.
>
> The general rule of thumb is that anything is a reasonable behavior to
> analyze if it is plausible of evidence about the users state of mind vis a
> vis the potential recommended actions.  Plausible initially means that a
> kinda sorta domain expert suggests the connection.  Plausible later means
> that the feature gets picked up as an indicator for some recommendations.
> If it never gets picked up, then it clearly isn't serving as a competitive
> piece of evidence about user intent.
>
>
>
>
> On Sat, Apr 26, 2014 at 6:46 PM, Pat Ferrel <pat@occamsmachete.com> wrote:
>
> > B = all item-sets gathered from user actions, actions like
> > purchased-together/shopping cart purchases, watchlists etc.
> > i = an item-set vector for a specific user
> >
> > B:
> > itemSetID, items
> > 1, iPad:iPad-case,stylus
> > 2, iPad:battery-booster:iPad-case
> >
> > [B’B]i = r_i, right?
> >
> > [B’B] would be an item-item cooccurrence similarity matrix taken from
> > item-set actions, calculated using LLR. The items-set IDs are not needed
> > anymore.
> >
> > This would imply that we could create an item-set indicator matrix, then
> > use a user’s item-set as the query to get back an ordered list taken from
> > cooccurrences in other items sets, rather than preference cooccurrences.
> >
> > So instead of summing similar items to each separate item in a shopping
> > cart to get an ordering of items to recommend (the way some people do
> > shopping cart recs) we could use the cooccurrence recommender to get
> these
> > directly from the items-sets. If the item-set is generated in near
> realtime
> > we’d need Solr (or some search engine) for the queries.
> >
> > The intuition being that things purchased together at the same time will
> > give you better shopping cart recs than using user preferences generally.
> > The item-sets often have something in common that user history will not
> > lead you to. I suppose you’d have to have a good size chunk of items-sets
> > to make it work.
> >
> > Does this make sense?
> >
> >
> >
>
>

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