Either architecture will work. Even if you want to pre-filter the data. The
search engine can post-filter in the query. The pre-filter is to create a
separate model for each day of the week, right? So works with any one.
If you are relying on the evaluator implemented in Mahout then use the
Efi,
Only you can really tell which is best for your efforts. All the rest is
our own partially informed opinions.
Pre-filtering can often be accomplished in the search context by creating
more than one indicator field and using different combinations of
indicators for different tasks. For
The by far easiest way to build a recommender (especially for production)
is to use the search engine approach (what Pat was recommending).
Post filtering can be done using the search engine far more easily than
using Java classes.
On Sat, Mar 7, 2015 at 8:44 AM, Pat Ferrel
Some of the references for the newer cooccurrence recommender that we now
suggest you use are at the top of the page here:
http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
There are many benefits of this new method including at its core a new
similarity algorithm that
On Sat, Mar 7, 2015 at 3:05 AM, Tevfik Aytekin tevfik.ayte...@gmail.com
wrote:
There can be two solutions:
1. There should be a parameter n, which determines the minimum number
of common ratings needed to compute a similarity otherwise the system
should return NaN.
2. The similarity should
Thanks for your help!
Actually, I want to build a recommender for experimental purposes following
the pre-filtering and post-filtering approaches that I described. I have
already two datasets and I want to show the benefits of using a
context-aware recommender. So,the recommender is going to work
