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From Sean Owen <>
Subject Re: Recommeding on Dynamic Content
Date Tue, 01 Feb 2011 09:52:19 GMT
One approach is to use user-user similarities. Those build up over time
based on historical data, but can be used to produce recommendations for
brand-new items going forward.

It still has a cold-start problem; until anyone connects to one of those new
items, it can't be recommended.

Another approach is to use the item's characteristics to determine some
notion of similarity, in the absence of clicks. That's what you're doing and
it's a great approach.

You can also consider hybrid approaches. You could try to mix
recommendations based on two different approaches -- clicks-based and
content-based. The problem is knowing how to mix things since the scores are
not at all comparable.

That Elkan / Menon paper has an elegant theoretical formulation of a
recommender that uses both ratings and side info at the same time.

On Mon, Jan 31, 2011 at 11:26 PM, Gökhan Çapan <> wrote:

> Hi,
> I've made a search, sorry in case this is a double post.
> Also, this question may not be directly related to Mahout.
> Within a domain which is enitrely user generated and has a very big item
> churn (lots of new items coming, while some others leaving the system),
> what
> do you recommend to produce accurate recommendations using Mahout (Not just
> Taste)?
> I mean, as a concrete example, in the eBay domain, not Amazon's.
> Currently I am creating item clusters using LSH with MinHash (I am not sure
> if it is in Mahout, I can contribute if it is not), and produce
> recommendations using these item clusters (profiles). When a new item
> arrives, I find its nearest profile, and recommend the item where its
> belonging profile is recommended to. Do you find this approach good enough?
> If you have a theoretical idea, could you please point me to some related
> papers?
> (As an MSc student, I can implement this as a Google Summer of Code
> project,
> with your mentoring.)
> Thanks in advance
> --
> Gokhan

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