The data that i sent you is just for testing purpose. The actual dataset is much larger and it is similar to this small data.
On Thu, Jul 16, 2009 at 12:56 PM, Sean Owen <[email protected]> wrote: > Yes, in one of your data sets, I noticed that all the preference > values were "1". This indicates to me that you really don't have a > notion of the strength of the preference between users and items. > There is an association, or there is none. I call this, somewhat > wrongly, a "boolean" preference. > > In this case, you can use faster and lighter versions of the > components you are currently using, which are specialized for this > situation. > > To try this, first use a copy of your data file which omits the final > ",1" on every line. You don't need it. > Instead of using PearsonCorrelationSimilarity, try > BooleanLogLikelihoodSimilarity. > Remove the PreferenceInferrer (these don't work so well anyway in my > experience) > Then use BooleanUserGenericUserBasedRecommender as your recommender > implementation. > > For such a small data set, it is already extremely fast. But if you > had a great deal more data, you would see a big difference. > > You may even find this approach, which ignores preference data, gives > better results. > > > On Thu, Jul 16, 2009 at 11:16 AM, Laya Patwa<[email protected]> > wrote: > > Thank you so much guys for discussing the problem of mine. I am getting > the > > recommendations now! > > You mentioned something about improving the performance in one of the > mails. >
