Hi,
Could this be due to a small number of items viewed/liked/purchased per
user?
Correct me if I'm wrong, but this would make the total recommendation space
sparse, thus making it hard to find good recommendations (ie
recommendations which are relevent and not obsolete). If so, it might be
Cassio,
I would implement a CandidateItemsStrategy that returns products that are
available now. A neighborhood based recommender would iterate over those
products, and rank them based on the similarity measure you provide.
If the DataModel of your recommender does not contain most of your
Assuming that most recent ratings or implicit preference data is more
important than the older ones, I wonder if there is a way to decrease the
importance (score) of old preference entries without having to update all
previous preferences.
Currently I'm fetching new preferences from time to time
Good idea Gokhan, thanks!
@ Sigbjørn: Thanks for the feedback. In fact we have plenty of [implicit]
preference data for each user, e.g. product view. What I found out is that
data from a certain point in time were very noisy and inconsistent, when I
started fetching from that point on I got much
Trying to integrate the Solr-recoemmender with the latest Mahout snapshot. The
project uses a modified RecommenderJob because it needs SequenceFile output and
to get the location of the preparePreferenceMatrix directory. If #1 and #2 are
addressed I can remove the modified Mahout code from the
If you have a lot of old historical data for products that no longer exist you
may be getting recommendations from that set. Using the old data is, in
principal fine and should make recs better. However you may be running into a
limit for the default number of recs returned, which is something
Hi Pat,
can you create issues for 1) and 2) ? Then I will try to get this into
trunk asap.
Best,
Sebastian
On 06.11.2013 19:13, Pat Ferrel wrote:
Trying to integrate the Solr-recoemmender with the latest Mahout snapshot.
The project uses a modified RecommenderJob because it needs
Done,
BTW I have the thing running on a demo site but am getting very poor results
that I think are related to the Solr setup. I’d appreciate any ideas.
The sample data has 27,000 items and something like 4000 users. The preference
data is fairly dense since the users are professional
