Hello! Have looked at the presentation and trying to get my head around it:
i. is collaborative filtering being bypassed?
ii. are new entries (observations) added dynamically or as a batch process?
From: Ted Dunning
Sent: Tuesday, November 23, 2010 8:10 AM
To: user@mahout.apache.org
Subject: Re: Matrixbased recommendation analysis
For cross recommender comprehension, I recommend something like the example
in my slide show. In that example, users issued query terms (giving the u x
q matrix B) and they watched videos (giving the u x v matrix A). The cross
recommendation is a smoothed version of A' B (which result is v x q).
This matrix could be used to take query terms and recommend videos. That is
(A' B) q = v. With suitable cleanup of the A'B to suppress spurious
entries, this makes a workable search engine.
Bringing a concept like days of the week into the mix is a bit confusing.
That could give you a smoothed popularity of content per day of the week,
but that is normally done by much simpler means. The biggest difference is
that you can't pick three days of the week, but you can put three terms into
a query.
On Tue, Nov 23, 2010 at 12:15 AM, Lance Norskog <goksron@gmail.com> wrote:
> I'm not trying to distinguish them. That is the "find the Netflix user"
> paper :)
>
> I just want to understand the crossrecommender concept, that's all.
> Yes, this sample is too small to impute "enthusiasm" the numbers are
> recommendation values.
>
> (If the rest of you want to follow along:
> http://www.slideshare.net/tdunning/intelligentsearch , slides 3536)
>
> On Mon, Nov 22, 2010 at 11:50 PM, Sean Owen <srowen@gmail.com> wrote:
> > (PS I don't think that link from Ted is publicly visible but try
> > http://www.slideshare.net/tdunning )
> >
> > Maybe I'm walking into half of a another conversation but what's the
> > question or goal here?
> >
> > I don't think the matrix product contains quite what you're saying.
> > For example U1 records only 2 ratings but has some "enthusiasm" on 3
> > separate days in the matrix product. The product is mashing together
> > itemday associations from all users and applying them to each user.
> >
> > Conceptually useritemday is the 3dimensional matrix that it sounds
> > like, if you want to distinguish associations from different users to
> > different items on different days.
> >
> >
> > On Tue, Nov 23, 2010 at 7:24 AM, Lance Norskog <goksron@gmail.com>
> wrote:
> >> The GroupLens dataset has User, Item, Rating and Timestamp.
> >> We will use the rating of 15 asis, but will reduce the timestamp
> >> field to day of the week.
> >> The lack of a rating defaults two 3 (neutral). There are 5 ratings
> >> total in the sample:
> >>
> >> U1, I1, 2, ?
> >> U1, I3, 4, ?
> >> U2, I1, 4, ?
> >> U2, I2, 5, T
> >> U2, I3, 3, ?
> >>
> >> (We'll get to the question marks later.)
> >> Now, make two matrices, User v.s. Item and Item v.s. Day of the Week.
> >> User v.s. Item contains ratings, and Item v.s. Day of the Week
> >> contains the number of rating records for that item on that day of the
> >> week: ratings only cover Sunday, Monday and Tuesday.
> >>
> >> Formatting tables in kerned fonts just plain doesn't work, thus the
> >> alternate format.
> >>
> >> 2 Users v.s. 3 Items:
> >> I1,I2,I3
> >> {
> >> U1 {2,3,4}
> >> U2 {4,5,3}
> >> }
> >>
> >> 3 Items v.s. 7 Days of the Week
> >> S,M,T,W,T,F,S
> >> {
> >> I1 {1,0,1,0,0,0,0}
> >> I2 {0,0,1,0,0,0,0}
> >> I3 {0,1,1,0,0,0,0}
> >> }
> >>
> >> Now, multiply these two matrices. The product is 2 Users v.s. 7 Days
> >> of the Week:
> >> S,M,T,W,T,F,S
> >> {
> >> U1 {2,4,9,0,0,0,0}
> >> U2 {4,3,12,0,0,0,0}
> >> }
> >>
> >> This matrix carries the total amount of enthusiasm for each user on
> >> each day. To get the average enthusiasm of each user, divide each row
> >> by the total number of ratings per day:
> >> S,M,T,W,T,F,S
> >> {
> >> U1 {2,4,3,0,0,0,0}
> >> U2 {4,3,4,0,0,0,0}
> >> }
> >>
> >> Did I get this right, Ted?
> >>
> >> BTW, where are your slides for this topic? I've seen them a couple of
> >> times in presentations (live and on Fora.tv), but can't find them.
> >>
> >> 
> >> Lance Norskog
> >> lance.norskog@gmail.com
> >>
> >
>
>
>
> 
> Lance Norskog
> goksron@gmail.com
>
