Hi again,
For collaborative filtering: www.stat.osu.edu/~dmsl/Sarwar_2001.pdf
For association rule learning:
http://rakesh.agrawal-family.com/papers/vldb94apriori.pdf
And as the schedule part:
- Getting familiar with scikit-learn, API structure etc. (1 week)
- Generating, finding datasets for future use. (1-3 days)
- Implementing association rule learning, (1 week)
- Testing, documenting (1 week)
- Implementing collaborative filtering (2 week)
- Testing, documenting (1 week)
- Evaluating the whole process, benchmarks, etc (1 week)
So in total 7 week + a couple of days is my plan for now.
I think timetable is okay when my knowledge on recommendation systems are
considered.
The only thing that I am lacking right now is scikit-learn know-how but I
already started learning, diving in it.
Any feedbacks are welcome !
Ps: I am working on commiting to the scikit-learn phase now.
On Wed, Apr 24, 2013 at 12:58 PM, Vlad Niculae <[email protected]> wrote:
> Thank you,
>
> Do you have some references prepared? It would be useful.
>
> I am not sure if what is in my head is correct but I think association
> rule learning is interesting and a kind of method that I would like to
> see in scikit-learn, as well as finding frequent itemsets. I hope I'm
> thinking of the right thing, though. I will use google but it would
> be great if you could provide us with the references that you are
> reading as well, so we can talk from the same place.
>
> Yours,
> Vlad
>
> On Wed, Apr 24, 2013 at 4:16 PM, Şükrü Bezen <[email protected]> wrote:
> > Hi Vlad,
> >
> > It looks good for me to focus on the proposal now and looking into mentor
> > later.
> >
> > I am considering collaborative filtering with user similarity and item
> > similarity.
> > And also association rule learning for finding out general behaviour of a
> > user-item group.
> >
> > I think those two would be good enough as the algorithms for a 3 month
> > period, what do you think ?
> >
> > I started my proposal but it is not finished yet, when it is finished
> (lets
> > say version-1) i will send it to get a feedback from you.
> > And about the scheduling part, I am working on that.
> >
> >
> >
> >
> > On Wed, Apr 24, 2013 at 6:23 AM, Vlad Niculae <[email protected]>
> wrote:
> >>
> >> Hi Şükrü
> >>
> >> We can focus on the proposal now and decide later who is better to
> >> mentor it. I could do it but it is not the thing I would be the best
> >> at mentoring, so to solve the chicken-and-egg problem we can optimize
> >> the decisions jointly when the time comes.
> >>
> >> Did you start working on your proposal and on a tentative schedule?
> >> Did you think of what algorithms you will implement?
> >>
> >> Also, regarding all of the other points made in the thread: even if
> >> merging into master is a good way to finish a GSoC, there is nothing
> >> wrong with leaving a project in a mergable state, but freezing it
> >> until 1.0 (which hopefully will not be very late!)
> >>
> >> Yours,
> >> Vlad
> >>
> >> On Sat, Apr 20, 2013 at 5:46 PM, Şükrü Bezen <[email protected]>
> wrote:
> >> > I am still looking for a mentor to backup this idea of mine, anyone
> >> > interested ?
> >> >
> >> >
> >> > On Wed, Apr 17, 2013 at 2:43 AM, Mathieu Blondel <
> [email protected]>
> >> > wrote:
> >> >>
> >> >>
> >> >>
> >> >> On Mon, Apr 15, 2013 at 10:45 PM, Olivier Grisel
> >> >> <[email protected]> wrote:
> >> >>>
> >> >>> Also I would rather avoid adding fancy new application specific
> public
> >> >>> API just for the recsys use case. Especially before the 1.0 release.
> >> >>> If we can stick to the existing public fit / transform / predict API
> >> >>> (using scipy.sparse matrices), then fine. Otherwise that might cause
> >> >>> trouble.
> >> >>
> >> >>
> >> >> I mentioned it in another thread but inverse_transform is exactly the
> >> >> method that we need to impute missing values.
> >> >>
> >> >> Mathieu
> >> >>
> >> >>
> >> >>
> >> >>
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> >> >
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> >> >
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