Hi,
I am currently trying to run the distributed Item Based Collaborative
filtering algorithm on our Hadoop cluster, and I have a few questions
regarding tweaking the various properties of the algorithm. For the
maxPrefsPerUser,maxSimilaritiesPerItem, and maxPrefsPerUserItemSimilarity
properties
Hi, Brian,
this question is also relevant for me. Perhaps somebody will give more
details because I am just learning myself. But, I guess you can try to
change the parameters, and check the performance, and write here about it
that everybody would get more knowledge!
In general, if these values a
Hi Brian *& *Miliauskas,
I am a data mining engineer form Taobao recommendation team. In past one
month, I have read all the code of mahout itemCF.
So maybe I can answer this question.
We consider the input of itemCF for one user is a item vector, like this
(the notation is from Json object model
Hi,
Thank you for the response! What you said makes sense. Here is a link to
the other property:
http://grepcode.com/file/repo1.maven.org/maven2/org.apache.mahout/mahout-core/0.6/org/apache/mahout/cf/taste/hadoop/item/RecommenderJob.java#RecommenderJob.0DEFAULT_MAX_SIMILARITIES_PER_ITEM
Supposi
Hi Brain,
The parameter "maxPrefsPerUserInItemSimilarity" is in RecommenderJob, from
the text of comment, It is the same as the paramter "maxPrefsPerUser " in
ItemSimilarityJob.
The second question is not easy to answer. It is decided by your
recommendation scenario and input data features. Th
Hi Brian,
Happy to give you some details:
So, from a matrix A (user x item) that holds user-item interactions,
this algorithm first computes a matrix S (item x item) of item
similarities and afterwards uses these item similarities to compute
recommendations for users.
the parameters refer to the
Hey Sebastian,
Thanks for all the information, that was very helpful. One question, when
you said "as large as the maximum number of interactions per user or
larger" for the macPrefsPerUser property does that refer if the algorithm
was comparing your items and my items it would be looking at how m
