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From Michael Sokolov <msoko...@safaribooksonline.com>
Subject Re: evaluating recommender with boolean prefs
Date Fri, 07 Jun 2013 19:57:04 GMT
```Thanks for your help

Yes, I think a time-based division of test v. training probably would
make sense since that will correspond to our actual intended practice.

But before I worry about that I seem to have some more fundamental
problem that is giving me 0 precision and 0 recall all the time...

-Mike

On 06/07/2013 02:58 PM, Sean Owen wrote:
> In point 1, I don't think I'd say it that way. It's not true that
> test/training is divided by user, because every user would either be
> 100% in the training or 100% in the test data. Instead you hold out
> part of the data for each user, or at least, for some subset of users.
> Then you can see whether recs for those users match the held out data.
>
> Yes then you see how the held-out set matches the predictions by
> computing ratios that give you precision/recall.
>
> The key question is really how you choose the test data. It's implicit
> data; one is as good as the next. In the framework I think it just
> randomly picks a subset of the data. You could also split by time;
> that's a defensible way to do it. Training data up to time t and test
> data after time t.
>
> On Fri, Jun 7, 2013 at 7:51 PM, Michael Sokolov
> <msokolov@safaribooksonline.com>  wrote:
>
>> I'm trying to evaluate a few different recommenders based on boolean
>> preferences.  The in action book suggests using an precision/recall metric,
>> but I'm not sure I understand what that does, and in particular how it is
>> dividing my data into test/train sets.
>>
>> What I think I'd like to do is:
>>
>> 1. Divide the test data by user: identify a set of training data with data
>> from 80% of the users, and test using the remaining 20% (say).
>>
>> 2. Build a similarity model from the training data
>>
>> 3. For the test users, divide their data in half; a "training" set and an
>> evaluation set.  Then for each test user, use their training data as input
>> to the recommender, and see if it recommends the data in the evaluation set
>> or not.
>>
>> Is this what the precision/recall test is actually doing?
>>
>> --
>> Michael Sokolov
>> Senior Architect
>> Safari Books Online
>>
>>

--
Michael Sokolov
Senior Architect
Safari Books Online

```
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