Hi, Thanks Pat for the reply.
I am trying to implement item based recommendation as the first step.When the user searches with a keyword(using Solr),not only it should return keyword matching results(already implemented along with other search features of Solr) but also related documents(recommended). I believe implementing item based recommendation will be a good learning curve towards implementing the user based recommendation or Behavioral based.As a first step I am trying to recommend min of two documents(As my Solr document index is ~100 docs). I understood that in the above scenario,first step is to provide the Solr index to mahout to read and will generate a vector file from it. It will be helpful if I get guidance on the integration steps to follow for the same. Thanks and Regards, Arun On 1 April 2017 at 23:46, Pat Ferrel <p...@occamsmachete.com> wrote: > You want to create “Behavioral Search”? This is where you boost items that > have the search terms in them more likely to be favored by the individual > user? > > You want to use the CCO algorithm in Mahout. You need to collect > behavioral information like conversions, detailed page views, etc. Run each > event through CCO and you get a collection of “indicators” as item > attributes. Augment the Solr index with fields (indicators) attached to > item documents. Then at query time supply the search terms as a “must > match” and use user history as the query segment against the corresponding > indicator fields as a “should match” with some boosting factor. > > CCO is here: http://mahout.apache.org/users/algorithms/intro- > cooccurrence-spark.html <http://mahout.apache.org/users/algorithms/intro- > cooccurrence-spark.html> > and a post on Personalizing Search here: http://www.actionml.com/blog/ > personalized_search <http://www.actionml.com/blog/personalized_search> > > BTW Do you have a recommender running? If not that is likely to generate > almost an order of magnitude better results than Behavioral Search. From > Industry wisdom and experience, implement a recommender first, then augment > search. On E-Commerce data we have reported results of 10-30% conversion > lift from recommendations and ~3% for Behavioral Search. 3% is significant > but requires you to gather the same info that it takes to do a recommender > so why not do a recommender first. > > There is an almost turnkey recommender that uses CCO here: > http://actionml.com/ur It uses Elasticsearch but is standalone, not > integrated into any search tech you use elsewhere. > > > On Mar 31, 2017, at 9:30 PM, arun abraham <arunabraham...@gmail.com> > wrote: > > Hi All, > > I am trying to integrate Apache mahout with Solr.I have created a search > application using Solr which has spellcheck,type ahead suggestions > functionalities.I have a new requirement to display recommendations( from > index which has ~100 docs ) for a specific search(keyword based).Is it > possible to recommend docs or links from web together with the indexed > data? > Kindly guide me on the possibilities for the same also on the integration > part. > > Thanks and Regards, > Arun > >
