@Tevfik, running this recommender:
GenericItemBasedRecommender itemRecommender = new
GenericItemBasedRecommender(dataModel, itemSimilarity, new
AllSimilarItemsCandidateItemsStrategy(itemSimilarity), new
AllSimilarItemsCandidateItemsStrategy(itemSimilarity));
With this dataModel:
1,1,1.0
1,2,2.0
1,3,1.0
1,4,2.0
2,1,1.0
2,2,4.0
And these similarities
1,2,0.1
1,3,0.2
1,4,0.3
2,3,0.5
3,4,0.5
5,1,0.2
5,2,1.0
Returns item 5 for User 1. So item 5 has not been preferred by user 1,
and
the similarity between item 5 and two of the items user 1 preferred are
not
NaN, but AllSimilarItemsCandidateItemsStrategy is returning that item.
So,
I'm truly sorry to insist on this, but I still really do not get the
difference.
On Wed, Mar 5, 2014 at 2:53 PM, Tevfik Aytekin
<tevfik.ayte...@gmail.com>wrote:
Juan,
You got me wrong,
AllSimilarItemsCandidateItemsStrategy
returns all items that have not been rated by the user and the
similarity metric returns a non-NaN similarity value with at
least one of the items preferred by the user.
So, it does not simply return all items that have not been rated by
the user. For example, if there is an item X which has not been rated
by the user and if the similarity value between X and at least one of
the items rated (preferred) by the user is not NaN, then X will be not
be returned by AllSimilarItemsCandidateItemsStrategy, but it will be
returned by AllUnknownItemsCandidateItemsStrategy.
On Wed, Mar 5, 2014 at 4:42 PM, Juan José Ramos <jjar...@gmail.com>
wrote:
Hi Tefik,
Thanks for the response. I think what you says contradicts what
Sebastian
pointed out before. Also, if AllSimilarItemsCandidateItemsStrategy
returns
all items that have not been rated by the user, what would
AllUnknownItemsCandidateItemsStrategy return?
On Wed, Mar 5, 2014 at 1:40 PM, Tevfik Aytekin
<tevfik.ayte...@gmail.com
wrote:
Sorry there was a typo in the previous paragraph.
If I remember correctly, AllSimilarItemsCandidateItemsStrategy
returns all items that have not been rated by the user and the
similarity metric returns a non-NaN similarity value with at
least one of the items preferred by the user.
On Wed, Mar 5, 2014 at 3:38 PM, Tevfik Aytekin <
tevfik.ayte...@gmail.com>
wrote:
Hi Juan,
If I remember correctly, AllSimilarItemsCandidateItemsStrategy
returns all items that have not been rated by the user and the
similarity metric returns a non-NaN similarity value that is with at
least one of the items preferred by the user.
Tevfik
On Wed, Mar 5, 2014 at 2:30 PM, Sebastian Schelter <s...@apache.org>
wrote:
On 03/05/2014 01:23 PM, Juan José Ramos wrote:
Thanks for the reply, Sebastian.
I am not sure if that should be implemented in the Abstract base
class
though because for
instance PreferredItemsNeighborhoodCandidateItemsStrategy, by
definition,
it returns the item not rated by the user and rated by somebody
else.
Good point. So we seem to need special implementations.
Back to my last post, I have been playing around with
AllSimilarItemsCandidateItemsStrategy
and AllUnknownItemsCandidateItemsStrategy, and although they both
do
what
I
wanted (recommend items not previously rated by any user), I
honestly
can't
tell the difference between the two strategies. In my tests the
output
was
always the same. If the eventual output of the recommender will not
include
items already rated by the user as pointed out here (
http://mail-archives.apache.org/mod_mbox/mahout-user/201403.mbox/%3CCABHkCkuv35dbwF%2B9sK88FR3hg7MAcdv0MP10v-5QWEvwmNdY%2BA%40mail.gmail.com%3E
),
AllSimilarItemsCandidateItemsStrategy should be equivalent to
AllUnkownItemsCandidateItemsStrategy, shouldn't it?
AllSimilarItems returns all items that are similar to any item that
the
user
already knows. AllUnknownItems simply returns all items that the
user
has
not interacted with yet.
These are two different things, although they might overlap in some
scenarios.
Best,
Sebastian
Thanks.
On Wed, Mar 5, 2014 at 10:23 AM, Sebastian Schelter <s...@apache.org
wrote:
Hi Juan,
that is a good catch. CandidateItemsStrategy is the right place to
implement this. Maybe we should simply extend its interface to add
a
parameter that says whether to keep or remove the current users
items?
We could even do this in the abstract base class then.
--sebastian
On 03/05/2014 10:42 AM, Juan José Ramos wrote:
In case somebody runs into the same situation, the key seems to
be in
the
CandidateItemStrategy being passed to the constructor
of GenericItemBasedRecommender. Looking into the code, if no
CandidateItemStrategy is specified in the
constructor, PreferredItemsNeighborhoodCandidateItemsStrategy is
used
and
as the documentation says, the doGetCandidateItems method:
"returns
all
items that have not been rated by the user and that were
preferred by
another user that has preferred at least one item that the
current
user
has
preferred too".
So, a different CandidateItemStrategy needs to be passed. For
this
problem,
it seems to me that AllSimilarItemsCandidateItemsStrategy,
AllUnknownItemsCandidateItemsStrategy are good candidates. Does
anybody
know where to find some documentation about the different
CandidateItemStrategy? Based on the name I would say that:
1) AllSimilarItemsCandidateItemsStrategy returns all similar
items
regardless of whether they have been already rated by someone or
not.
2) AllUnknownItemsCandidateItemsStrategy returns all similar
items
that
have not been rated by anyone yet.
Does anybody know if it works like that?
Thanks.
On Tue, Mar 4, 2014 at 9:16 AM, Juan José Ramos <
jjar...@gmail.com>
wrote:
First thing is thatI know this requirement would not make sense
in
a CF
Recommender. In my case, I am trying to use Mahout to create
something
closer to a Content-Based Recommender.
In particular, I am pre-computing a similarity matrix between
all
the
documents (items) of my catalogue and using that matrix as the
ItemSimilarity for my Item-Based Recommender.
So, when a user rates a document, how could I make the
recommender
outputs
similar documents to that ones the user has already rated even
if no
other
user in the system has rated them yet? Is that even possible in
the
first
place?
Thanks a lot.
