First it looks like some misspelled IDs ipad != iPad iphone != iPhone
Second you have to treat purchase as the primary action and view as the secondary action this will create two indicator matrices in two different directories as the docs say. Use the command line in the docs for two actions. Notice: --filter1 purchase \ # word that flags input for the primary action --filter2 view \ # word that flags input for the secondary action This tells the job to create an indicator matrix from lines with “purchase” and a cross-indicator from lines with “view” Read the "More Complex Input” section. On Sep 19, 2014, at 1:27 AM, pol <[email protected]> wrote: Hi Pat, Thank you very much! I had a little understanding. In this example： item purchase view -------------------------------------------------------- galaxy nexus galaxy iphone nexus iPad surface surface nexus iPhone ipad galaxy iphone nexus ipad nexus galaxy galaxy iphone nexus ipad iPad iphone galaxy iphone nexus iPad When a user view "surface", "surface" recommended for him to view; When a user purchase "nexus" and "iPad", "galaxy" and "iPhone" recommended for him to purchase; Of course, there is no filtering for recommendation result. I understand is right? Thanks. On Sep 19, 2014, at 04:40, Pat Ferrel <[email protected]> wrote: > You create the indicator and cross-indicator matrices with —omitStrength then > if you are using a database with solr or elasticsearch you will create a > table: > > item ID, list of indicator Item IDs, list of cross-indicator item IDs > > 3 columns. All IDs will be like “nexus” in the example—they are your > application’s item IDs. The second and third column contain lists of item > IDs. There are several ways you can do this either by using a multi-valued > field (array of IDs) or a space delimited string depending on how you want to > integrate with your search engine and database. Check the instructions for > your particular search engine. > > At the time you want to recommend, take the user’s history of the primary > action (purchase in the example) and map it to the "list of indicator Item > IDs” field. Take the user’s history of the secondary action (view in the > example) and map it to the "list of cross-indicator Item IDs” field. Then > perform the search engine query and you’ll get back a list of item IDs to > recommend. Filter out any items that the user has in their history (if you > wish) and recommend the items in the order they were returned > > This blog explains more: > http://occamsmachete.com/ml/2014/09/09/mahout-on-spark-whats-new-in-recommenders-part-2/ > > Ted’s book gives an example architecture: > https://www.mapr.com/practical-machine-learning > > On Sep 18, 2014, at 10:00 AM, pol <[email protected]> wrote: > > Hi, All > I saw spark-itemsimilarity doc at > http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html, but > I don’t understand how can creating a recommender by spark-itemsimilarity? I > don’t understand "3 Creating a Recommender" chapter. > For input of the form: > u1,purchase,iphone > u1,purchase,ipad > u2,purchase,nexus > u2,purchase,galaxy > u3,purchase,surface > u4,purchase,iphone > u4,purchase,galaxy > u1,view,iphone > u1,view,ipad > u1,view,nexus > u1,view,galaxy > u2,view,iphone > u2,view,ipad > u2,view,nexus > u2,view,galaxy > u3,view,surface > u3,view,nexus > u4,view,iphone > u4,view,ipad > u4,view,galaxy > output > out-path > |-- indicator-matrix - TDF part files > \-- cross-indicator-matrix - TDF part-files > The indicator matrix will contain the lines: > galaxy\tnexus:1.7260924347106847 > ipad\tiphone:1.7260924347106847 > nexus\tgalaxy:1.7260924347106847 > iphone\tipad:1.7260924347106847 > surface > The cross-indicator matrix will contain: > iphone\tnexus:1.7260924347106847 iphone:1.7260924347106847 > ipad:1.7260924347106847 galaxy:1.7260924347106847 > ipad\tnexus:0.6795961471815897 iphone:0.6795961471815897 > ipad:0.6795961471815897 galaxy:0.6795961471815897 > nexus\tnexus:0.6795961471815897 iphone:0.6795961471815897 > ipad:0.6795961471815897 galaxy:0.6795961471815897 > galaxy\tnexus:1.7260924347106847 iphone:1.7260924347106847 > ipad:1.7260924347106847 galaxy:1.7260924347106847 > surface\tsurface:4.498681156950466 nexus:0.6795961471815897 > ————---- > Now，u4 view nexus, how to recommend for u4 by the above of output? > > Thanks. > > > > >
