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
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
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 all,
I have a product recommendation use case for an e-commerce site and I've
been playing around mahout's CF capabilities lately. I reached a point
where I should ask a feedback from the community on my approach. I'm
struggling to get ANY recommendation.
Here's what I've done so far:
1)
