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From Vaghawan Ojha <>
Subject Re: Need a Suggessations
Date Thu, 23 Mar 2017 19:25:30 GMT
Hi Pat,

Thank you very much.Yes I will be following actionml instruction since I'm
going to use UR. I think I should rather direct myself to HBASE rather than
expensing time  in setting up Mysql. Part of my need is that once we train
the dataset, the result should be easily available to the application which
are running into Mysql.

I'm fairly new to the concept itself. So basically I would always have a
larage json file coming from the application which uses mysql(this
shouldn't be the problem). Then I would use PIO and UR to do the hard work,
and get back the result either like an API which I think already works in
PIO or saved somewhere in database like mysql or something like that.


On Fri, Mar 24, 2017 at 1:03 AM, Pat Ferrel <> wrote:

> The UR uses Elasticsearch for part of the Recommender algorithm, therefor
> it must be configured as a storage backend. It is possible to use Postgres
> or MySQL for the other stores but we have very little experience with this.
> HBase is indefinitely scalable so we always use that. Single machine
> deployments are rare with a reasonably sized data so Elasticsearch + Hbase
> running separately or in clusters will always meet the data needs. The RDBs
> will not and anyway, like I said you have to use Elasticsearch.
> Therefore for the UR follow instructions on the ActionML site since they
> are specific to the UR. For other templates you may use other
> configurations of PIO but if you use the UR config you can also use every
> template too.
> On Mar 23, 2017, at 9:07 AM, Vaghawan Ojha <> wrote:
> Hi, Thank you!
> I came into further more confusion here, actually I installed prediction
> IO version 0.10.0 from here http://predictionio.
>  and have been fighting
> to configure mysql as a storage in my local linux machine.
> But I see there is a different documentation of installing in actionml
> website, I'm not sure for which I would have to go. Currently there is no "
>".  file inside conf folder however there is
> file. I commented the pgsql section and uncommented the
> mysql section with the username and password, but whenever I do . sudo
> PredictionIO-0.10.0-incubating/bin/pio eventserver there seems to be an
> error that says that authentication failed with pgsql, however I don't want
> to use pgsql.
> # Storage Repositories
> # Default is to use PostgreSQL
> # Storage Data Sources
> # PostgreSQL Default Settings
> # Please change "pio" to your database name in
> #PIO_STORAGE_SOURCES_PGSQL_URL=jdbc:postgresql://localhost/pio
> # MySQL Example
>  PIO_STORAGE_SOURCES_MYSQL_URL=jdbc:mysql://localhost/pio
> This is how the looks like. And again when I visited
> the actionml site, it suggests that I do have to have ELASTICSEARCH. but
> site doesn't tells us the same. Which one should I follow
> and where would I find the current working version of installation guide. I
> actually wanaa use in my production shortly after I
> implemented in local.
> Please help me, thank you very much for your help, I appreciate it so much.
> Vaghawan
> On Thu, Mar 23, 2017 at 9:27 PM, Pat Ferrel <> wrote:
>> Since PIO has moved to Apache, the namespace of PIO code changed and so
>> all templates need to be updated. None of the ones in
>> <>
>> work with Apache PIO. For the upgraded UR see:
>> ml/universal-recommender Docs for the UR are here:
>> Also look on the Template gallery page here for a description of template
>> status. Some have not been moved to the new namespace and converted to run
>> with PIO but this is pretty easy to do yourself. http://predictionio.
>> user_id, product_id and purchase_date is all you need to use any
>> recommender. If you plan to gather other events in the future, use the UR.
>> As far as item or user based recommendations, the UR will give either based
>> on the query with the same data and model, as some others will do. The UR
>> allows you to mix both types in a single query, which may be useful with
>> small amounts of individual user data.
>> Also the accepted wisdom about this it to put item-based recs on item
>> detail pages, and user-based recs elsewhere, when you don’t have an item to
>> base recs on, or in another placement on any page.
>> You can have many different placements of recs in any page by changing
>> the queries. This is how Netflix gets rows and rows of specialized recs for
>> different things all based on the same data. The UR queries are quite
>> flexible.
>> On Mar 23, 2017, at 7:08 AM, Vaghawan Ojha <> wrote:
>> Hi,
>> I've been trying to deploy a recommendation system using
>> l-universal-recommendation.
>> I've purchase history of user something like this:
>> user_id, product_id and purchase_date, so I will be using user_id and
>> product_id to determine the recommendation. I'm not sure if I would be able
>> to customize the default even parameter.
>> Do you have any suggestions like which template would be more suitable
>> for my problem. I don't have data like rating or view state, I only have
>> data about user and product they purchased. I need something like item
>> based similarity as well as user based item similarity.
>> Any help would be great
>> Thank you
>> Vaghawan

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