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I guess one dataset that can really be helpful is the one that was made
available for KDD Cup 2012, Track 1.

http://www.kddcup2012.org/c/kddcup2012-track1

The prediction task involves predicting whether or not a user will follow
> an item that has been recommended to the user. Items can be persons,
> organizations, or groups and will be defined more thoroughly below.


Thanks and Regards


On Tue, Jan 21, 2014 at 8:22 PM, Denis Parra <[log in to unmask]> wrote:

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> Dear Vasiliki,
>
> I work in recommender systems for some years and is not usual to find a
> dataset with the actual logs of recommendations provided and the users
> acceptance of the recommendations.
>
> I am not sure how familiar you are with evaluating recommender systems'
> accuracy but the usual way to do it (at least using an off-line dataset,
> different story is with a user study) is to obtain a dataset with users and
> their preferences over certain items (a ground truth where commonly the
> preferences are ratings or some form of implicit feedback: playcounts,
> clicks, etc.) and you proceed like in a usual prediction task in data
> mining: split your data for train/test (or train/validation/test), for the
> training part you can build any model you want (a recommendation model:
> collaborative filtering, content-based, hybrid, most-popular, etc.), then
> you evaluate your predictions over the test part. You report you results
> after cross-validation.
>
> Usual datasets are Movielens , jester jokes , netflix prize (you can find
> some of them in grouplens page http://grouplens.org/datasets/ but check
> also SNAP datasets http://snap.stanford.edu/data/ )
>
> Again, I don't know you expertise evaluating recommenders, but in case you
> have not much experience I suggest you to read:
> - Evaluating collaborative filtering recommender systems by Herlocker et
> al. (2004) and
> - Evaluating Recommender Systems by Guy Shani and Asela Gunawardana (2009) if
> you have time, you can also read my book chapter:
> Denis Parra, Shaghayegh Sahebi. Recommender Systems: Sources of Knowledge
> and Evaluation Metrics Chapter 7 in Advanced Techniques in Web
> Intelligence-2: Web User Browsing Behaviour and Preference Analysis, Ed.
> Juan Velasquez et al., Springer-Verlag, 2013 (I can send it to you if you
> can't access it)
>
> Hope that helps. Cheers,
>
> Denis Parra
> Assistant Professor, CS Department
> School of Engineering, PUC Chile
> web.ing.puc.cl/~dparra/
>
>
>
>
> On Tue, Jan 21, 2014 at 9:51 AM, Vasiliki Pouli <[log in to unmask]> wrote:
>
>> *****  To join INSNA, visit http://www.insna.org  *****
>>
>> Dear all,
>> I am a member of a research group in the National Technical University of
>> Athens and I am working on recommendation systems. I am interested in
>> evaluating the accuracy of recommendations and I am looking for datasets
>> that provide recommendations (any kind) and also provide information about
>> how many of these recommendations are accepted by the users.
>> I would like to ask if anyone is aware of such a dataset.
>>
>> Thank you.
>> Vasiliki
>>
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>
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-- 
Hemank Lamba
http://menetworked.wordpress.com/
https://sites.google.com/site/hemanklamba/home

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