small free book here, which talks about the general idea: https://www.mapr.com/practical-machine-learning
preso, which talks about mixing actions or other indicators: http://occamsmachete.com/ml/2014/10/07/creating-a-unified-recommender-with-mahout-and-a-search-engine/
two blog posts: http://occamsmachete.com/ml/2014/08/11/mahout-on-spark-whats-new-in-recommenders/ http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/
mahout docs: http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
Build Mahout from this source: https://github.com/apache/mahout
This will run stand-alone on a dev machine, then if your data is too big for a single machine you can run it on a Spark + Hadoop cluster. The data this creates can be put into a DB or indexed directly by a search engine (Solr or Elasticsearch). Choose the search engine you want then queries of a user’s item id history will go there--results will be an ordered list of item ids to recommend.
The core piece is the command line job: “mahout spark-itemsimilarity”, which can parse csv data. The options specify what columns are used for ids.
Start out simple by looking only at user and item IDs. Then you can add other cross-cooccurrence indicators for multiple actions later pretty easily.
On Nov 28, 2014, at 12:14 AM, Yash Patel <[EMAIL PROTECTED]> wrote:
The mahout + search engine recommender seems what would be best for the
data i have.
Kindly get back to me at your earliest convenience.
On Thu, Nov 27, 2014 at 9:58 PM, Pat Ferrel <[EMAIL PROTECTED]> wrote: