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From Ted Dunning <>
Subject Re: recommend ads using mahout?
Date Wed, 04 Apr 2012 14:18:26 GMT
The current state of the art in ad recognition is contextual bandits backed up by logistic
or probit regression. The mahout logistic regression is a decent first step on this but probably
doesn't provide the necessary accuracy.  

I have some early work on the bandit algorithms on github but this is still early work. 

I think that using a recommender with ad features only would give you a very weak ad targeting
algorithm because of the high level of ad churn and generally poor quality of ad meta data.

Sent from my iPhone

On Apr 4, 2012, at 4:44 AM, Sean Owen <> wrote:

> I would recommend you use (only) the ad data. These are "boolean" data
> points in the recommender engine speak. You can 'recommend' ads this
> way.
> I understand your question is a bit more than that. First you want to
> use the *not*-clicked data. My first question is, is this meaningful?
> I am served 1000 ads per day that I don't even look at; that I do not
> click them does not say much. Is your situation some kind of
> interstitial ad that the user is forced to skip? that's more
> meaningful, but the same comment applies.
> If you really do have such meaningful data, consider making a separate
> "anti-recommender" out of this data. This will tell you which ads are
> probably worst to show. You could merge the two results then to make
> your decision.
> What to do with purchase data? You could ignore it on the grounds that
> when recommending ads, the only thing that matter is its ability to
> induce a click -- whether it results in a purchase is a different
> matter.
> Or you could view it as reaffirming that the ad click was a "strong
> click", that it is more likely the user was not merely curious or
> mis-clicked, but was significantly more interested in the advertised
> product.
> You could go back and add "ratings" to your model -- a "1" for a click
> and a "5" for a click that results in purchase? It's quite arbitrary
> and I don't know if the results are much better.
> If you're serious about using this data too, I would again recommend
> looking at the ALS algorithm as presented in
>  -- their model is nice in
> that it ingests a "confidence" in the association between a user and
> item, which is much more like what you have than a "rating".
> On Wed, Apr 4, 2012 at 10:35 AM, vinutha <> wrote:
>> Hello!
>> I have a data set containing user behavior such as which products s/he
>> clicked on , and which products s/he bought from a retail site. I have
>> another data set containing which ads the same user has clicked on, and
>> the
>> ads which were shown to him/her but hasn't been clicked on. The idea is to
>> use the user behavior data set to make recommendations for ads.
>> As I ve understood from Mahout in Action, there isn't a way to introduce
>> user behavior has a feature set . One can only use, userid, productid /ad
>> id
>> , preferences.
>> Is my understanding correct?
>> Any suggestions would be most welcome!
>> Thanks,
>> Vinutha
>> --
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