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From Lance Norskog <goks...@gmail.com>
Subject Re: Recommendations without explicit ratings
Date Sat, 22 Oct 2011 02:59:23 GMT
The Lucene/Solr MoreLikeThis feature is (I believe) a cosine distance search
across multiple fields of documents. Depending on the domain, its results
may be useful or surreal.

Lance

On Fri, Oct 21, 2011 at 1:20 AM, Sean Owen <srowen@gmail.com> wrote:

> Great point, yes, you could easily use a text search engine to come up
> with a similarity, if the things are text-like documents.
> These aren't recs by themselves, but the similarities can plug in to
> the item-based recommender easily.
>
> On Fri, Oct 21, 2011 at 4:12 AM, Octavian Covalschi
> <octavian.covalschi@gmail.com> wrote:
> > I'm not an expert but I do have a comment on B). Similarity between meta
> > data can be achieved by using some kind of search engine. For this kind
> of
> > functionality I'm using SOLR (http://wiki.apache.org/solr/MoreLikeThis),
> it
> > has a builtin feature that would give ya similar documents. All you have
> to
> > give it is a doc id... However I think this won't be a real
> recommendation,
> > since similar items may not be something that user want... for example if
> I
> > bought an expensive camera, I may not need any more similar items, right?
> > But in the same time, if I'm buying batteries every half a year.. I may
> be
> > interested in similar products.... so it depends.
> >
> > Just a thought.
> >
> >
> > On Thu, Oct 20, 2011 at 4:30 PM, Sean Owen <srowen@gmail.com> wrote:
> >
> >> On Thu, Oct 20, 2011 at 10:13 PM, Camilo Rostoker
> >> <camilorostoker@hotmail.com> wrote:
> >> > A) Use an item-based recommender, with the rating being the number of
> >> times they bought the item (perhaps normalize the data between 1-10).
> >>
> >> Yes, good. My first reaction might be to use the logarithm of number
> >> of purchases, or ignore it altogether and just record the association
> >> (a 'boolean' pref) regardless of the purchase count. This only makes a
> >> complete system together with B) or C) though.
> >>
> >> >
> >> > B) Use the meta-data to generate similarities between the items, then
> >> simply recommend to a user the top N items that are similar to one that
> >> they've previously purchased.  This could be implemented in Mahout by
> >> overriding the ItemSimilarity (as described in this post:
> >>
> http://lucene.472066.n3.nabble.com/Content-based-Recommender-Implementation-td913981.html
> ).
> >>   Obviously the challenging part here is figuring out how to generate a
> >> similarity score for the two items using the meta-data.
> >>
> >> Exactly. You can plug in whatever you logic you want there, but
> >> equally you have to make up that logic. To start, you can experiment
> >> with simplistic rules like considering only items in the same category
> >> "similar". It might do reasonably well as a start.
> >>
> >> You can of course just use purchases, pure collaborative filtering, to
> >> generate similarity. For instance log-likelihood similarity works
> >> well.
> >>
> >>
> >> >
> >> > C) Use frequent item-sets to figure out other items that are usually
> >> bought with that one, and recommend those.
> >>
> >> You could use frequent item sets to determine item-item similarity, as
> >> in B). That's kind of what log-likelihood is doing. This would then be
> >> a plug-in similarity to your item-based algorithm in A).
> >>
> >> If you mean you just want to start with an *item*, and find similar
> >> items, sure you can do that. This is simpler than the full recommender
> >> problem.
> >>
> >
>



-- 
Lance Norskog
goksron@gmail.com

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