recommend ads using mahout?

[vinutha <vi...@yahoo.com>]

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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Re: recommend ads using mahout?

[Ted Dunning <te...@gmail.com>]

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 <sr...@gmail.com> 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
> www2.research.att.com/~yifanhu/PUB/cf.pdf  -- 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 <vi...@yahoo.com> 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
>> 
>> --
>> View this message in context:
>> http://lucene.472066.n3.nabble.com/recommend-ads-using-mahout-tp3883496p3883496.html
>> Sent from the Mahout User List mailing list archive at Nabble.com.

Re: recommend ads using mahout?

[Sean Owen <sr...@gmail.com>]

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
www2.research.att.com/~yifanhu/PUB/cf.pdf  -- 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 <vi...@yahoo.com> 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
>
> --
> View this message in context:
> http://lucene.472066.n3.nabble.com/recommend-ads-using-mahout-tp3883496p3883496.html
> Sent from the Mahout User List mailing list archive at Nabble.com.