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Date: Fri, 21 Oct 2011 09:20:00 +0100
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 <CAEccTywt8n+eFWPK+wHjJB7wwsqyF-VmKPuQA9YddeDCAa4iHQ@mail.gmail.com>
Subject: Re: Recommendations without explicit ratings
From: Sean Owen <srowen@gmail.com>
To: user@mahout.apache.org
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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 o=
f
> 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 recommendatio=
n,
> 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 b=
e
> 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. =C2=A0This could be implemented in Mahout =
by
>> overriding the ItemSimilarity (as described in this post:
>> http://lucene.472066.n3.nabble.com/Content-based-Recommender-Implementat=
ion-td913981.html).
>> =C2=A0 Obviously the challenging part here is figuring out how to genera=
te 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.
>>
>