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.
>
>
>
>
>
