Hi Pat,
I made a spelling mistake, As you said, I am a reference to this
example:
http://mahout.apache.org/users/recommender/intro-cooccurrence-spark.html
I know, I understand is right. Thank you again.
On Sep 19, 2014, at 22:14, Pat Ferrel <[email protected]> wrote:
> 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.
>>
>>
>>
>>
>>
>
>
>
