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Posted to user@mahout.apache.org by Pat Ferrel <pa...@occamsmachete.com> on 2014/09/09 16:28:42 UTC
New Mahout Recommender Service
Now that we have the basis of several significant improvements to Mahout’s recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
Is anyone interested in discussing this here?
RE: New Mahout Recommender Service
Posted by Saikat Kanjilal <sx...@hotmail.com>.
Thanks Pat, would love to be included in any smaller group google hangout type discussions.
> Subject: Re: New Mahout Recommender Service
> From: pat@occamsmachete.com
> Date: Tue, 9 Sep 2014 09:16:04 -0700
> To: user@mahout.apache.org
>
> No Jira yet. There are too many moving parts and we’d have to see if it’s appropriate for Mahout inclusion or as an “example” project. It would be great to include but we’ll have to see what others think as it takes better form. All components should be Apache license compatible though.
>
> I’ll start a Github project. Does anyone object to using this list for discussion?
>
> On Sep 9, 2014, at 8:46 AM, Saikat Kanjilal <sx...@hotmail.com> wrote:
>
> @Pat Any interest in using http://vertx.io instead of play, have heard some really good perf stats around this
>
> We should really start a jira with a list of use cases and then back into a tech stack and outline the design in jira, thoughts ?
>
> Sent from my iPhone
>
> > On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
> >
> > Would absolutely love an ES integration.
> >
> > -----Original Message-----
> > From: Pat Ferrel [mailto:pat@occamsmachete.com]
> > Sent: Tuesday, September 09, 2014 10:29 AM
> > To: user@mahout.apache.org
> > Subject: New Mahout Recommender Service
> >
> > Now that we have the basis of several significant improvements to Mahout's recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
> >
> > Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
> >
> > The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
> >
> > Is anyone interested in discussing this here?
>
Re: New Mahout Recommender Service
Posted by Phil Wills <ot...@gmail.com>.
Hi Pat,
This would be very, very useful to me. I've been looking into this
recently, seeking what you describe and have hit exactly the complexities
you mention. Would love to keep up with any such effort and see if there's
any way I can contribute.
Phil
On Wed, Sep 10, 2014 at 5:59 PM, Pat Ferrel <pa...@occamsmachete.com> wrote:
> Firstly I can’t imagine reimplementing anything in Solr or Eleasticsearch
> this would integrate with them unmodified just as Mahout integrates with
> databases. In fact I see them as similarity based key-value DBs.
>
> Partly I’m reacting to the fact that if mrlegacy were deprecated at some
> time in the future, Mahout would not have a recommender. And yet it is,
> from looking at Stackoverflow and the archive of this list, one of the most
> used Mahout components.
>
> Partly I’d like to properly implement in one place all of the innovations
> that we have been talking about around cooccurrence recommenders, namely:
> * blending collaborative filtering, metadata, and content recommenders
> into one
> * multiple actions to recommend the primary action making use of all
> available user behavior
> * noise injected into recs ordering to widen the exposure of items and as
> a form of self-tuning (dithering)
>
> This leaves a couple needed features to support those above:
> * ingesting and data prep to suport the above features in near realtime
> (for new user recs and recency of action support)
> * an API for these features that fits their use
> * implemented as a server to allow virtually any client to use it
>
> At first cut it is something you can point to user interactions and then
> can pass in a user ID to get back item IDs. At this point the only thing
> that does this in mahout is the in-memory recommender. To do this with new
> users and realtime gathered preferences we need to add a new thing to the
> system, streaming data prep, probably with Spark streaming, though this is
> a discussion all by itself.
>
> This does something like what Solr and Elasticsearch do for Lucene. We
> take several of Mahout’s algos and a search server directly without
> modification and implement only what is missing to produce a modern
> recommender with major innovations.
>
> Is this something useful to users?
>
>
> On Sep 9, 2014, at 5:38 PM, Ted Dunning <te...@gmail.com> wrote:
>
> What would the advantage be of a dedicated server over a search engine like
> solr or elastic search?
>
> It seems that you would be replicating much of the effort just to build a
> server that does nearly the same thing.
>
>
>
> On Tue, Sep 9, 2014 at 5:32 PM, Peng Zhang <pz...@gmail.com> wrote:
>
> > Using this list to discuss is very convenient to stay tuned, so no
> > objection.
> >
> > Peng Zhang
> >
> > --
> > Sent from my iPhone
> >
> >> On Sep 10, 2014, at 12:16 AM, Pat Ferrel <pa...@occamsmachete.com> wrote:
> >>
> >> No Jira yet. There are too many moving parts and we’d have to see if
> > it’s appropriate for Mahout inclusion or as an “example” project. It
> would
> > be great to include but we’ll have to see what others think as it takes
> > better form. All components should be Apache license compatible though.
> >>
> >> I’ll start a Github project. Does anyone object to using this list for
> > discussion?
> >>
> >> On Sep 9, 2014, at 8:46 AM, Saikat Kanjilal <sx...@hotmail.com>
> wrote:
> >>
> >> @Pat Any interest in using http://vertx.io instead of play, have heard
> > some really good perf stats around this
> >>
> >> We should really start a jira with a list of use cases and then back
> > into a tech stack and outline the design in jira, thoughts ?
> >>
> >> Sent from my iPhone
> >>
> >>> On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
> >>>
> >>> Would absolutely love an ES integration.
> >>>
> >>> -----Original Message-----
> >>> From: Pat Ferrel [mailto:pat@occamsmachete.com]
> >>> Sent: Tuesday, September 09, 2014 10:29 AM
> >>> To: user@mahout.apache.org
> >>> Subject: New Mahout Recommender Service
> >>>
> >>> Now that we have the basis of several significant improvements to
> > Mahout's recommender it seems like we need to go the last step and
> provide
> > a service. Without this it is left to the user to do a lot of integration
> > making the current next gen somewhat incomplete.
> >>>
> >>> Using the Hadoop mapreduce code you can get all recs for all people
> > using collaborative filtering data or you can use the in-memory single
> > machine recommender if you have a small dataset.
> >>>
> >>> The next generation would require Solr or Elasticsearch so why not go
> > the extra step and provide a recommender API on top? At very least it
> would
> > give users a single machine API they can call, analogous to the in-memory
> > recommender of Mahout 0.9. But it would also be indefinitely scalable.
> >>>
> >>> Is anyone interested in discussing this here?
> >>
> >
>
>
Re: New Mahout Recommender Service
Posted by Pat Ferrel <pa...@occamsmachete.com>.
Firstly I can’t imagine reimplementing anything in Solr or Eleasticsearch this would integrate with them unmodified just as Mahout integrates with databases. In fact I see them as similarity based key-value DBs.
Partly I’m reacting to the fact that if mrlegacy were deprecated at some time in the future, Mahout would not have a recommender. And yet it is, from looking at Stackoverflow and the archive of this list, one of the most used Mahout components.
Partly I’d like to properly implement in one place all of the innovations that we have been talking about around cooccurrence recommenders, namely:
* blending collaborative filtering, metadata, and content recommenders into one
* multiple actions to recommend the primary action making use of all available user behavior
* noise injected into recs ordering to widen the exposure of items and as a form of self-tuning (dithering)
This leaves a couple needed features to support those above:
* ingesting and data prep to suport the above features in near realtime (for new user recs and recency of action support)
* an API for these features that fits their use
* implemented as a server to allow virtually any client to use it
At first cut it is something you can point to user interactions and then can pass in a user ID to get back item IDs. At this point the only thing that does this in mahout is the in-memory recommender. To do this with new users and realtime gathered preferences we need to add a new thing to the system, streaming data prep, probably with Spark streaming, though this is a discussion all by itself.
This does something like what Solr and Elasticsearch do for Lucene. We take several of Mahout’s algos and a search server directly without modification and implement only what is missing to produce a modern recommender with major innovations.
Is this something useful to users?
On Sep 9, 2014, at 5:38 PM, Ted Dunning <te...@gmail.com> wrote:
What would the advantage be of a dedicated server over a search engine like
solr or elastic search?
It seems that you would be replicating much of the effort just to build a
server that does nearly the same thing.
On Tue, Sep 9, 2014 at 5:32 PM, Peng Zhang <pz...@gmail.com> wrote:
> Using this list to discuss is very convenient to stay tuned, so no
> objection.
>
> Peng Zhang
>
> --
> Sent from my iPhone
>
>> On Sep 10, 2014, at 12:16 AM, Pat Ferrel <pa...@occamsmachete.com> wrote:
>>
>> No Jira yet. There are too many moving parts and we’d have to see if
> it’s appropriate for Mahout inclusion or as an “example” project. It would
> be great to include but we’ll have to see what others think as it takes
> better form. All components should be Apache license compatible though.
>>
>> I’ll start a Github project. Does anyone object to using this list for
> discussion?
>>
>> On Sep 9, 2014, at 8:46 AM, Saikat Kanjilal <sx...@hotmail.com> wrote:
>>
>> @Pat Any interest in using http://vertx.io instead of play, have heard
> some really good perf stats around this
>>
>> We should really start a jira with a list of use cases and then back
> into a tech stack and outline the design in jira, thoughts ?
>>
>> Sent from my iPhone
>>
>>> On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
>>>
>>> Would absolutely love an ES integration.
>>>
>>> -----Original Message-----
>>> From: Pat Ferrel [mailto:pat@occamsmachete.com]
>>> Sent: Tuesday, September 09, 2014 10:29 AM
>>> To: user@mahout.apache.org
>>> Subject: New Mahout Recommender Service
>>>
>>> Now that we have the basis of several significant improvements to
> Mahout's recommender it seems like we need to go the last step and provide
> a service. Without this it is left to the user to do a lot of integration
> making the current next gen somewhat incomplete.
>>>
>>> Using the Hadoop mapreduce code you can get all recs for all people
> using collaborative filtering data or you can use the in-memory single
> machine recommender if you have a small dataset.
>>>
>>> The next generation would require Solr or Elasticsearch so why not go
> the extra step and provide a recommender API on top? At very least it would
> give users a single machine API they can call, analogous to the in-memory
> recommender of Mahout 0.9. But it would also be indefinitely scalable.
>>>
>>> Is anyone interested in discussing this here?
>>
>
Re: New Mahout Recommender Service
Posted by Ted Dunning <te...@gmail.com>.
What would the advantage be of a dedicated server over a search engine like
solr or elastic search?
It seems that you would be replicating much of the effort just to build a
server that does nearly the same thing.
On Tue, Sep 9, 2014 at 5:32 PM, Peng Zhang <pz...@gmail.com> wrote:
> Using this list to discuss is very convenient to stay tuned, so no
> objection.
>
> Peng Zhang
>
> --
> Sent from my iPhone
>
> > On Sep 10, 2014, at 12:16 AM, Pat Ferrel <pa...@occamsmachete.com> wrote:
> >
> > No Jira yet. There are too many moving parts and we’d have to see if
> it’s appropriate for Mahout inclusion or as an “example” project. It would
> be great to include but we’ll have to see what others think as it takes
> better form. All components should be Apache license compatible though.
> >
> > I’ll start a Github project. Does anyone object to using this list for
> discussion?
> >
> > On Sep 9, 2014, at 8:46 AM, Saikat Kanjilal <sx...@hotmail.com> wrote:
> >
> > @Pat Any interest in using http://vertx.io instead of play, have heard
> some really good perf stats around this
> >
> > We should really start a jira with a list of use cases and then back
> into a tech stack and outline the design in jira, thoughts ?
> >
> > Sent from my iPhone
> >
> >> On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
> >>
> >> Would absolutely love an ES integration.
> >>
> >> -----Original Message-----
> >> From: Pat Ferrel [mailto:pat@occamsmachete.com]
> >> Sent: Tuesday, September 09, 2014 10:29 AM
> >> To: user@mahout.apache.org
> >> Subject: New Mahout Recommender Service
> >>
> >> Now that we have the basis of several significant improvements to
> Mahout's recommender it seems like we need to go the last step and provide
> a service. Without this it is left to the user to do a lot of integration
> making the current next gen somewhat incomplete.
> >>
> >> Using the Hadoop mapreduce code you can get all recs for all people
> using collaborative filtering data or you can use the in-memory single
> machine recommender if you have a small dataset.
> >>
> >> The next generation would require Solr or Elasticsearch so why not go
> the extra step and provide a recommender API on top? At very least it would
> give users a single machine API they can call, analogous to the in-memory
> recommender of Mahout 0.9. But it would also be indefinitely scalable.
> >>
> >> Is anyone interested in discussing this here?
> >
>
Re: New Mahout Recommender Service
Posted by Peng Zhang <pz...@gmail.com>.
Using this list to discuss is very convenient to stay tuned, so no objection.
Peng Zhang
--
Sent from my iPhone
> On Sep 10, 2014, at 12:16 AM, Pat Ferrel <pa...@occamsmachete.com> wrote:
>
> No Jira yet. There are too many moving parts and we’d have to see if it’s appropriate for Mahout inclusion or as an “example” project. It would be great to include but we’ll have to see what others think as it takes better form. All components should be Apache license compatible though.
>
> I’ll start a Github project. Does anyone object to using this list for discussion?
>
> On Sep 9, 2014, at 8:46 AM, Saikat Kanjilal <sx...@hotmail.com> wrote:
>
> @Pat Any interest in using http://vertx.io instead of play, have heard some really good perf stats around this
>
> We should really start a jira with a list of use cases and then back into a tech stack and outline the design in jira, thoughts ?
>
> Sent from my iPhone
>
>> On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
>>
>> Would absolutely love an ES integration.
>>
>> -----Original Message-----
>> From: Pat Ferrel [mailto:pat@occamsmachete.com]
>> Sent: Tuesday, September 09, 2014 10:29 AM
>> To: user@mahout.apache.org
>> Subject: New Mahout Recommender Service
>>
>> Now that we have the basis of several significant improvements to Mahout's recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>>
>> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>>
>> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>>
>> Is anyone interested in discussing this here?
>
Re: New Mahout Recommender Service
Posted by Pat Ferrel <pa...@occamsmachete.com>.
No Jira yet. There are too many moving parts and we’d have to see if it’s appropriate for Mahout inclusion or as an “example” project. It would be great to include but we’ll have to see what others think as it takes better form. All components should be Apache license compatible though.
I’ll start a Github project. Does anyone object to using this list for discussion?
On Sep 9, 2014, at 8:46 AM, Saikat Kanjilal <sx...@hotmail.com> wrote:
@Pat Any interest in using http://vertx.io instead of play, have heard some really good perf stats around this
We should really start a jira with a list of use cases and then back into a tech stack and outline the design in jira, thoughts ?
Sent from my iPhone
> On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
>
> Would absolutely love an ES integration.
>
> -----Original Message-----
> From: Pat Ferrel [mailto:pat@occamsmachete.com]
> Sent: Tuesday, September 09, 2014 10:29 AM
> To: user@mahout.apache.org
> Subject: New Mahout Recommender Service
>
> Now that we have the basis of several significant improvements to Mahout's recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>
> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>
> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>
> Is anyone interested in discussing this here?
Re: New Mahout Recommender Service
Posted by Saikat Kanjilal <sx...@hotmail.com>.
@Pat Any interest in using http://vertx.io instead of play, have heard some really good perf stats around this
We should really start a jira with a list of use cases and then back into a tech stack and outline the design in jira, thoughts ?
Sent from my iPhone
> On Sep 9, 2014, at 8:44 AM, "Martin, Nick" <Ni...@pssd.com> wrote:
>
> Would absolutely love an ES integration.
>
> -----Original Message-----
> From: Pat Ferrel [mailto:pat@occamsmachete.com]
> Sent: Tuesday, September 09, 2014 10:29 AM
> To: user@mahout.apache.org
> Subject: New Mahout Recommender Service
>
> Now that we have the basis of several significant improvements to Mahout's recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>
> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>
> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>
> Is anyone interested in discussing this here?
RE: New Mahout Recommender Service
Posted by "Martin, Nick" <Ni...@pssd.com>.
Would absolutely love an ES integration.
-----Original Message-----
From: Pat Ferrel [mailto:pat@occamsmachete.com]
Sent: Tuesday, September 09, 2014 10:29 AM
To: user@mahout.apache.org
Subject: New Mahout Recommender Service
Now that we have the basis of several significant improvements to Mahout's recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
Is anyone interested in discussing this here?
Re: New Mahout Recommender Service
Posted by Saikat Kanjilal <sx...@hotmail.com>.
I have expressed interest in the past in building a recommender based on elasticsearch, I can see building an API that performs targeted queries on top of recommendations as a service endpoint.
Sent from my iPhone
> On Sep 9, 2014, at 7:29 AM, "Pat Ferrel" <pa...@occamsmachete.com> wrote:
>
> Now that we have the basis of several significant improvements to Mahout’s recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>
> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>
> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>
> Is anyone interested in discussing this here?
Re: New Mahout Recommender Service
Posted by Pat Ferrel <pa...@occamsmachete.com>.
yes
The demo https://guide.finderbots.com has a “trainer” and returns recs immediately after you indicate some preferences (using the trainer or just browsing and hitting thumbs up). It is not implemented in the way we’d want for a general recommender though and that is what I’d like to do.
On Sep 9, 2014, at 7:36 AM, Peng Zhang <pz...@gmail.com> wrote:
That will be a great feature.
Currently if the offline brach job will run hours to update the recs. Can this api update recs in realtime? i.e. can we update the recs for a user based on her last few behaviors 5 minutes ago?
On Sep 9, 2014, at 10:28 PM, Pat Ferrel <pa...@occamsmachete.com> wrote:
> Now that we have the basis of several significant improvements to Mahout’s recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>
> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>
> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>
> Is anyone interested in discussing this here?
Re: New Mahout Recommender Service
Posted by Pat Ferrel <pa...@gmail.com>.
Solr and Elasticsearch have near realtime index updates and the indicators can be batch calculated and updated on the order of every few minutes. Spark has streaming for handling new user actions and all this is scalable.
Too me the first step is just being able to query and get recs from a service. Ultimately this needs to support the new ideas we’ve been talking about or it will just be a “me too” project. These innovations do not exist in any recommender that I know of.
Those new things include:
* blending collaborative filtering, metadata, and content both for training and in the recs returning query
* handle multiple actions to recommend the primary action
* noise injected into recs to widen the exposure of items and as a form of self-tuning
I was thinking to use Play + AngularJS to create a minimal UI and implement the REST + JSON API.
On Sep 9, 2014, at 7:45 AM, Saikat Kanjilal <sx...@hotmail.com> wrote:
Maybe we can use spark to do the updates in real time to the recs
Sent from my iPhone
> On Sep 9, 2014, at 7:37 AM, "Peng Zhang" <pz...@gmail.com> wrote:
>
> That will be a great feature.
>
> Currently if the offline brach job will run hours to update the recs. Can this api update recs in realtime? i.e. can we update the recs for a user based on her last few behaviors 5 minutes ago?
>
>
>> On Sep 9, 2014, at 10:28 PM, Pat Ferrel <pa...@occamsmachete.com> wrote:
>>
>> Now that we have the basis of several significant improvements to Mahout’s recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>>
>> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>>
>> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>>
>> Is anyone interested in discussing this here?
>
Re: New Mahout Recommender Service
Posted by Saikat Kanjilal <sx...@hotmail.com>.
Maybe we can use spark to do the updates in real time to the recs
Sent from my iPhone
> On Sep 9, 2014, at 7:37 AM, "Peng Zhang" <pz...@gmail.com> wrote:
>
> That will be a great feature.
>
> Currently if the offline brach job will run hours to update the recs. Can this api update recs in realtime? i.e. can we update the recs for a user based on her last few behaviors 5 minutes ago?
>
>
>> On Sep 9, 2014, at 10:28 PM, Pat Ferrel <pa...@occamsmachete.com> wrote:
>>
>> Now that we have the basis of several significant improvements to Mahout’s recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>>
>> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>>
>> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>>
>> Is anyone interested in discussing this here?
>
Re: New Mahout Recommender Service
Posted by Peng Zhang <pz...@gmail.com>.
That will be a great feature.
Currently if the offline brach job will run hours to update the recs. Can this api update recs in realtime? i.e. can we update the recs for a user based on her last few behaviors 5 minutes ago?
On Sep 9, 2014, at 10:28 PM, Pat Ferrel <pa...@occamsmachete.com> wrote:
> Now that we have the basis of several significant improvements to Mahout’s recommender it seems like we need to go the last step and provide a service. Without this it is left to the user to do a lot of integration making the current next gen somewhat incomplete.
>
> Using the Hadoop mapreduce code you can get all recs for all people using collaborative filtering data or you can use the in-memory single machine recommender if you have a small dataset.
>
> The next generation would require Solr or Elasticsearch so why not go the extra step and provide a recommender API on top? At very least it would give users a single machine API they can call, analogous to the in-memory recommender of Mahout 0.9. But it would also be indefinitely scalable.
>
> Is anyone interested in discussing this here?