> I think it would be surprising behavior for a recommender to return data it
already knows; I just think the implicit contract is to return only
predictions. That's how real-world recommender systems appear to behave, to
the end user; Amazon doesn't show you books you have already read, even if
indeed they may be some of your favorites ever.
The current recommender algorithms do this.
SlopeOneRecommender: estimatePreference(), line 123:
public float estimatePreference(long userID, long itemID) throws
TasteException {
DataModel model = getDataModel();
Float actualPref = model.getPreferenceValue(userID, itemID);
if (actualPref != null) {
return actualPref;
}
return doEstimatePreference(userID, itemID);
}
As do all of the other recommenders, where the algorithm makes it
possible. This makes <1% difference in the RMSE.
On Sat, Jan 28, 2012 at 12:33 PM, Ted Dunning <[email protected]> wrote:
> Also, Lee, I think you have it backwards. It is true that clicks are not
> the same thing as preferences, but I don't think that I give a fig about
> preferences since they are the internal mental state of the visitor. I
> know that some of the visitors are mental, but I don't really care since
> that is their own business. What I care about is encouraging certain
> behaviors.
>
> So I find that mental state estimations are the indirect way to model and
> predict behaviors while directly modeling behaviors based on observed
> behaviors is, well, more direct.
>
> This is compounded by the fact that asking people to rate things invokes a
> really complicated social interaction which does not directly sample the
> internal mental state (i.e. the real preference) but instead measures
> behavior that is a very complicated outcome of social pressures,
> expectations and the internal mental state. So using ratings boils down to
> using one kind of behavior to estimate mental state that then is
> hypothesized to result in the desired second kind of behavior.
>
>
>
> On Sat, Jan 28, 2012 at 10:51 AM, Sean Owen <[email protected]> wrote:
>
>> It means *something* that a user clicked on one item and not 10,000 others.
>> You will learn things like that Star Wars and Star Trek are somehow related
>> from this data. I don't think that clicks are a bad input per se.
>>
>> I agree that it's not obvious how to meaningfully translate user actions
>> into a linear scale. "1" per click and "10" for purchase or something is a
>> guess. I do think you will learn something from the data this way.
>>
>> There is nothing conceptually wrong with mixing real data and estimated
>> data. If the results aren't looking right, it is not a problem with the
>> concept, but the mapping of action onto some rating scale. I think it's
>> hard to get that right, but is not impossible to get it "good".
>>
>> On Sat, Jan 28, 2012 at 10:15 AM, Lee Carroll
>> <[email protected]>wrote:
>>
>> > > I would argue, though, that .recommend() is aimed at the latter task:
>> >
>> > No . I think the mismatch here is you are using at best a wild guess
>> > at a preference for the convenience of using a recommender and then in
>> > the same breath expecting the recommender to "understand" that you are
>> > not using preferences at all and actually have no idea what the user
>> > preference is. You cant have it both ways :-)
>> >
>> > A click through on an item is not a measure of user preference for
>> > that item. I know its not what you want to hear (or better what your
>> > business users want to here) but there it is.
>> >
>> > We can pretend, or maybe even build a convincing narrative that a
>> > click is some sort of item association and use that as a proxy
>> > preference and we might even get some mileage out of it, but we should
>> > not change the behaviour of the .recommend() to hide its short
>> > comings.
>> >
>>
--
Lance Norskog
[email protected]
