On Nov 10, 2008, at 12:05 PM, Sean Owen wrote:
Fine by me, I had always distributed it myself. I pulled it out here
by
request I think in order to reduce the number of dependencies.
I would rather not remove working, plausibly useful functionality
just to
avoid a dependency - web services are definitely in that category.
At worst
just make it a dependency you have to add, which is what I did here
(same
for EJB though not sure anyone actually uses that). Obviously I have
no
issue going the other way and including Axis.
OK, I agree on keeping it. I can see Mahout growing to have a webapp
layer that isn't just for taste, so maybe, eventually, we will want to
separate out the webapp part from the "core"
On explanation - I like the idea. I am struggling to think of any
commonality between explanations across all algorithms. That may argue
against a generic Explanation object but not against specific
implementations tailored for particular algorithms.
I am then trying to imagine what the explanation is like. For user-
based
recommenders it is really a neighborhood of users that explains it.
Do you
return that? In an object that tells you stuff like how much a given
item is
liked by the group?
I like the idea... when I dig in I am having trouble thinking of
just what
an explanation looks like. I can imagine reporting interesting
figures (e.g.
most popular items in neighborhood) that don't really explain things.
Perhaps it is good to think of real-world examples like amazon's
'recommended because' function. That I already expose - in the
algorithm for
which it makes any sense that is. For instance there would be no
such notion
of this in slope-one.
An interesting item to mull over indeed. It is a way a recommender
system
could add real insight and value.
I think you might be going a little to generic in your thinking. The
only real difference between the actual recommendation and the explain
of that recommendation is that the explain provides the details of why
the recommendation was given. For instance, and I'm just making this
up, it might say something like:
Item A had a score of 2.5, which is the product of
Recommender Weight Factor: 0.5 times
# of Users recommending item: 10 times
Avg. Recommendation Score: 0.5
In Lucene, an explain looks like:
0.34713835 = (MATCH) sum of:
0.29951444 = weight(text:"i pod" in 0), product of:
0.54212046 = queryWeight(text:"i pod"), product of:
5.8931932 = idf(text: i=27 pod=25)
0.091990955 = queryNorm
0.5524869 = fieldWeight(text:"i pod" in 0), product of:
1.0 = tf(phraseFreq=1.0)
5.8931932 = idf(text: i=27 pod=25)
0.09375 = fieldNorm(field=text, doc=0)
0.04762391 = (MATCH) sum of:
0.04762391 = (MATCH) weight(text:gb in 0), product of:
0.21617201 = queryWeight(text:gb), product of:
2.3499267 = idf(docFreq=48, numDocs=33)
0.091990955 = queryNorm
0.22030562 = (MATCH) fieldWeight(text:gb in 0), product of:
1.0 = tf(termFreq(text:gb)=1)
2.3499267 = idf(docFreq=48, numDocs=33)
0.09375 = fieldNorm(field=text, doc=0)
(FYI: I got this by running the Solr example and using the query:
http://localhost:8983/solr/select?indent=on&version=2.2&q=iPod+AND+GB&start=0&rows=10&fl=*%2Cscore&qt=standard&wt=standard&debugQuery=on&explainOther=&hl.fl=)
Due note, I don't even know if this makes sense in CF, but I suspect
it does, otherwise we wouldn't be having this conversation. :-)
On 10 Nov 2008, 1:54 PM, "Grant Ingersoll" <[EMAIL PROTECTED]>
wrote:
On Nov 10, 2008, at 8:32 AM, Sean Owen wrote: > The web service bit
won't
work without Axis but ye...
Yep, it does. Axis is ASF, so we can just put them in where they
need to
be. Either that, or let's not support Web Services.
You hit on an interesting feature of the slope-one recommender
used in
the demo. It does h...
Right, the same is true for Lucene. We have an abstract "explain"
method
that gets implemented by the various scoring pieces. So, for
Taste, we
could probably add it to the Recommender class, something like:
abstract Explanation explain(Item, Item, ???); //Given a source item,
explain why the recommended Item was chosen.
and
abstract Explanation explain(User, User, Item, ???); //Given a
source user,
explain why the item was recommended for the target user.
On Mon, Nov 10, 2008 at 1:21 PM, Grant Ingersoll
<[EMAIL PROTECTED]>
wrote: >> >> Hi, >> >> ...
--------------------------
Grant Ingersoll
Lucene Boot Camp Training Nov. 3-4, 2008, ApacheCon US New Orleans.
http://www.lucenebootcamp.com
Lucene Helpful Hints:
http://wiki.apache.org/lucene-java/BasicsOfPerformance
http://wiki.apache.org/lucene-java/LuceneFAQ
--------------------------
Grant Ingersoll
Lucene Helpful Hints:
http://wiki.apache.org/lucene-java/BasicsOfPerformance
http://wiki.apache.org/lucene-java/LuceneFAQ
