***** To join INSNA, visit http://www.insna.org ***** 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: > ***** To join INSNA, visit http://www.insna.org ***** > 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 >> >> _____________________________________________________________________ >> SOCNET is a service of INSNA, the professional association for social >> network researchers (http://www.insna.org). To unsubscribe, send >> an email message to [log in to unmask] containing the line >> UNSUBSCRIBE SOCNET in the body of the message. >> > > _____________________________________________________________________ > SOCNET is a service of INSNA, the professional association for social > network researchers (http://www.insna.org). To unsubscribe, send an email > message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET > in the body of the message. > -- Hemank Lamba http://menetworked.wordpress.com/ https://sites.google.com/site/hemanklamba/home _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.