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From Anatoliy Kats <>
Subject Re: Add on to itemsimilarity
Date Thu, 26 Jan 2012 08:28:25 GMT
I have not seen this discussion from the beginning, but I think the 
troubles I'm having are similar in nature.  We are recommending items 
the user can buy on our website.  Our preferences are past purchases, 
and also past clicks on the item's description.  If a purchase was made, 
certainly we do not want to recommend the item again, but if it was only 
a click, we are even more confident that we should be recommending that 
item.  Yet the recommenders are hardcoded not to.  I managed to get 
around this by changing the recommender's CandidateItemSimilarity.

I also need to estimatePreference() of the items the user clicked on, or 
at least I think I do.  The unclicked items have an estimated preference 
of around 0.3, whereas the click is treated as a rating of 1.  
Intuitively that seems unfair, I'd essentially only be recommending 
items the user clicked on.  I have my own recommender class which uses 
Generic...Recommender() as a delegate.  So, I can override the 
estimatePreference() to return something else, but this concerns me for 
two reasons.  First, this is not estimatePreference()'s intended usage, 
so I'm afraid of breaking something.  Second, many recommenders have a 
private doEstimatePreference() method that I'd love to call for 
already-rated items, but since it is a private method of my delegate, I 
cannot.  That makes me sad.

I hope this helps some of you, and I would appreciate some feedback on 
whether what I'm doing is even a good idea, and how to go about it.



On 01/25/2012 09:36 PM, Sean Owen wrote:
> (moving to user@)
> I think I understand more about what you are doing. It doesn't quite make
> sense to say you will train a recommender on the output of the recommender,
> but I understand that you mean you have some information about what users
> have visited what attractions or shows.
> This is classic recommendation. You put that in, and it can tell you what
> other attractions, shows, etc. the user may like.
> So going back to the beginning, I'm not yet clear on why that isn't already
> the answer for you, since you have built this. Explain again what else you
> are trying to do to filter or process the result?
> On Wed, Jan 25, 2012 at 5:25 PM, Saikat Kanjilal<>wrote:
>> Putting back on the list, we want to recommend new items in the park, an
>> item could be:1) attraction2) restaurant3) show4) Ride5) resort
>> Our real data if you will is the recommendations that result from
>> understanding their preferences in more detail based on their reservations
>> and resort stays.  So I wonder if our real data is our training data that
>> the recommender can use for training and calculate predicted data based on
>> that.
>> Date: Wed, 25 Jan 2012 17:20:02 +0000
>> Subject: Re: Add on to itemsimilarity
>> From:
>> To:
>> (do you mind putting this back on the list? might be a good discussion for
>> others)
>> What are you recommending to the user -- theme parks, rides at a theme
>> park?
>> Yes, you would always be recommending 'unknown' things to the user. You
>> already 'know' how much they like or dislike the things for which you have
>> data, so recommendations aren't of use to you.
>> Of course, you can use both real and predicted data in your system -- it
>> depends on what you are trying to accomplish. The recommender's role is
>> creating the predicted data.
>> On Wed, Jan 25, 2012 at 5:12 PM, Saikat Kanjilal<>
>> wrote:
>> Actually let me more clear, we are building a recommendations engine for a
>> theme parks experience,  the user preferences is something we are storing
>> based on the user's reservations and analytics, this is something that's
>> stored before the user rates any items and may or may not have a direct
>> relationship to the recommendations the user makes as they go around the
>> park.  This is due to the fact that the user recommendations could be other
>> rides or attractions that exist outside of the actual preferences.  Its not
>> clear yet to me how to tie these preferences into the item similarity
>> results.

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