Re: Software stack for Recommendation engine with spark mlib
Hi , Just 2 follow up questions, please suggest 1. Is there any commercial recommendation engine apart from the open source tools(Mahout,Spark) that are available that anybody can suggest ? 2. In this case only the purchase transaction is captured. There are no ratings and no feedback available, no page views calculated by the application, so in this case how far the recommendation engine will be effective in recommending similar products to a user. What are the features that should be available in order to create a robust recommendation engine. e.g product views etc, please kindly suggest a few features that should be available. Is purchase enough? Thanks in advance. On Sun, Mar 15, 2015 at 6:16 PM, Sean Owen so...@cloudera.com wrote: I think you're assuming that you will pre-compute recommendations and store them in Mongo. That's one way to go, with certain tradeoffs. You can precompute offline easily, and serve results at large scale easily, but, you are forced to precompute everything -- lots of wasted effort, not completely up to date. The front-end part of the stack looks right. Spark would do the model building; you'd have to write a process to score recommendations and store the result. Mahout is the same thing, really. 500K items isn't all that large. Your requirements aren't driven just by items though. Number of users and latent features matter too. It matters how often you want to build the model too. I'm guessing you would get away with a handful of modern machines for a problem this size. In a way what you describe reminds me of Wibidata, since it built recommender-like solutions on top of data and results published to a NoSQL store. You might glance at the related OSS project Kiji (http://kiji.org/) for ideas about how to manage the schema. You should have a look at things like Nick's architecture for Graphflow, however it's more concerned with computing recommendation on the fly, and describes a shift from an architecture originally built around something like a NoSQL store: http://spark-summit.org/wp-content/uploads/2014/07/Using-Spark-and-Shark-to-Power-a-Realt-time-Recommendation-and-Customer-Intelligence-Platform-Nick-Pentreath.pdf This is also the kind of ground the oryx project is intended to cover, something I've worked on personally: https://github.com/OryxProject/oryx -- a layer on and around the core model building in Spark + Spark Streaming to provide a whole recommender (for example), down to the REST API. On Sun, Mar 15, 2015 at 10:45 AM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Hi, Can anyone who has developed recommendation engine suggest what could be the possible software stack for such an application. I am basically new to recommendation engine , I just found out Mahout and Spark Mlib which are available . I am thinking the below software stack. 1. The user is going to use Android app. 2. Rest Api sent to app server from the android app to get recommendations. 3. Spark Mlib core engine for recommendation engine 4. MongoDB database backend. I would like to know more on the cluster configuration( how many nodes etc) part of spark for calculating the recommendations for 500,000 items. This items include products for day care etc. Other software stack suggestions would also be very useful.It has to run on multiple vendor machines. Please suggest. Thanks shashi
Re: Software stack for Recommendation engine with spark mlib
Thanks Sean, your suggestions and the links provided are just what I needed to start off with. On Sun, Mar 15, 2015 at 6:16 PM, Sean Owen so...@cloudera.com wrote: I think you're assuming that you will pre-compute recommendations and store them in Mongo. That's one way to go, with certain tradeoffs. You can precompute offline easily, and serve results at large scale easily, but, you are forced to precompute everything -- lots of wasted effort, not completely up to date. The front-end part of the stack looks right. Spark would do the model building; you'd have to write a process to score recommendations and store the result. Mahout is the same thing, really. 500K items isn't all that large. Your requirements aren't driven just by items though. Number of users and latent features matter too. It matters how often you want to build the model too. I'm guessing you would get away with a handful of modern machines for a problem this size. In a way what you describe reminds me of Wibidata, since it built recommender-like solutions on top of data and results published to a NoSQL store. You might glance at the related OSS project Kiji (http://kiji.org/) for ideas about how to manage the schema. You should have a look at things like Nick's architecture for Graphflow, however it's more concerned with computing recommendation on the fly, and describes a shift from an architecture originally built around something like a NoSQL store: http://spark-summit.org/wp-content/uploads/2014/07/Using-Spark-and-Shark-to-Power-a-Realt-time-Recommendation-and-Customer-Intelligence-Platform-Nick-Pentreath.pdf This is also the kind of ground the oryx project is intended to cover, something I've worked on personally: https://github.com/OryxProject/oryx -- a layer on and around the core model building in Spark + Spark Streaming to provide a whole recommender (for example), down to the REST API. On Sun, Mar 15, 2015 at 10:45 AM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Hi, Can anyone who has developed recommendation engine suggest what could be the possible software stack for such an application. I am basically new to recommendation engine , I just found out Mahout and Spark Mlib which are available . I am thinking the below software stack. 1. The user is going to use Android app. 2. Rest Api sent to app server from the android app to get recommendations. 3. Spark Mlib core engine for recommendation engine 4. MongoDB database backend. I would like to know more on the cluster configuration( how many nodes etc) part of spark for calculating the recommendations for 500,000 items. This items include products for day care etc. Other software stack suggestions would also be very useful.It has to run on multiple vendor machines. Please suggest. Thanks shashi
Re: Software stack for Recommendation engine with spark mlib
I think you're assuming that you will pre-compute recommendations and store them in Mongo. That's one way to go, with certain tradeoffs. You can precompute offline easily, and serve results at large scale easily, but, you are forced to precompute everything -- lots of wasted effort, not completely up to date. The front-end part of the stack looks right. Spark would do the model building; you'd have to write a process to score recommendations and store the result. Mahout is the same thing, really. 500K items isn't all that large. Your requirements aren't driven just by items though. Number of users and latent features matter too. It matters how often you want to build the model too. I'm guessing you would get away with a handful of modern machines for a problem this size. In a way what you describe reminds me of Wibidata, since it built recommender-like solutions on top of data and results published to a NoSQL store. You might glance at the related OSS project Kiji (http://kiji.org/) for ideas about how to manage the schema. You should have a look at things like Nick's architecture for Graphflow, however it's more concerned with computing recommendation on the fly, and describes a shift from an architecture originally built around something like a NoSQL store: http://spark-summit.org/wp-content/uploads/2014/07/Using-Spark-and-Shark-to-Power-a-Realt-time-Recommendation-and-Customer-Intelligence-Platform-Nick-Pentreath.pdf This is also the kind of ground the oryx project is intended to cover, something I've worked on personally: https://github.com/OryxProject/oryx -- a layer on and around the core model building in Spark + Spark Streaming to provide a whole recommender (for example), down to the REST API. On Sun, Mar 15, 2015 at 10:45 AM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Hi, Can anyone who has developed recommendation engine suggest what could be the possible software stack for such an application. I am basically new to recommendation engine , I just found out Mahout and Spark Mlib which are available . I am thinking the below software stack. 1. The user is going to use Android app. 2. Rest Api sent to app server from the android app to get recommendations. 3. Spark Mlib core engine for recommendation engine 4. MongoDB database backend. I would like to know more on the cluster configuration( how many nodes etc) part of spark for calculating the recommendations for 500,000 items. This items include products for day care etc. Other software stack suggestions would also be very useful.It has to run on multiple vendor machines. Please suggest. Thanks shashi - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
Software stack for Recommendation engine with spark mlib
Hi, Can anyone who has developed recommendation engine suggest what could be the possible software stack for such an application. I am basically new to recommendation engine , I just found out Mahout and Spark Mlib which are available . I am thinking the below software stack. 1. The user is going to use Android app. 2. Rest Api sent to app server from the android app to get recommendations. 3. Spark Mlib core engine for recommendation engine 4. MongoDB database backend. I would like to know more on the cluster configuration( how many nodes etc) part of spark for calculating the recommendations for 500,000 items. This items include products for day care etc. Other software stack suggestions would also be very useful.It has to run on multiple vendor machines. Please suggest. Thanks shashi
Re: Software stack for Recommendation engine with spark mlib
Thanks Nick, for your suggestions. On Sun, Mar 15, 2015 at 10:41 PM, Nick Pentreath nick.pentre...@gmail.com wrote: As Sean says, precomputing recommendations is pretty inefficient. Though with 500k items its easy to get all the item vectors in memory so pre-computing is not too bad. Still, since you plan to serve these via a REST service anyway, computing on demand via a serving layer such as Oryx or PredictionIO (or the newly open sourced Seldon.io) is a good option. You can also cache the recommendations quite aggressively - once you compute a user or item top-K list, just stick the result in mem cache / redis / whatever and evict it when you recompute your offline model, or every hour or whatever. — Sent from Mailbox https://www.dropbox.com/mailbox On Sun, Mar 15, 2015 at 3:03 PM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Thanks Sean, your suggestions and the links provided are just what I needed to start off with. On Sun, Mar 15, 2015 at 6:16 PM, Sean Owen so...@cloudera.com wrote: I think you're assuming that you will pre-compute recommendations and store them in Mongo. That's one way to go, with certain tradeoffs. You can precompute offline easily, and serve results at large scale easily, but, you are forced to precompute everything -- lots of wasted effort, not completely up to date. The front-end part of the stack looks right. Spark would do the model building; you'd have to write a process to score recommendations and store the result. Mahout is the same thing, really. 500K items isn't all that large. Your requirements aren't driven just by items though. Number of users and latent features matter too. It matters how often you want to build the model too. I'm guessing you would get away with a handful of modern machines for a problem this size. In a way what you describe reminds me of Wibidata, since it built recommender-like solutions on top of data and results published to a NoSQL store. You might glance at the related OSS project Kiji (http://kiji.org/) for ideas about how to manage the schema. You should have a look at things like Nick's architecture for Graphflow, however it's more concerned with computing recommendation on the fly, and describes a shift from an architecture originally built around something like a NoSQL store: http://spark-summit.org/wp-content/uploads/2014/07/Using-Spark-and-Shark-to-Power-a-Realt-time-Recommendation-and-Customer-Intelligence-Platform-Nick-Pentreath.pdf This is also the kind of ground the oryx project is intended to cover, something I've worked on personally: https://github.com/OryxProject/oryx -- a layer on and around the core model building in Spark + Spark Streaming to provide a whole recommender (for example), down to the REST API. On Sun, Mar 15, 2015 at 10:45 AM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Hi, Can anyone who has developed recommendation engine suggest what could be the possible software stack for such an application. I am basically new to recommendation engine , I just found out Mahout and Spark Mlib which are available . I am thinking the below software stack. 1. The user is going to use Android app. 2. Rest Api sent to app server from the android app to get recommendations. 3. Spark Mlib core engine for recommendation engine 4. MongoDB database backend. I would like to know more on the cluster configuration( how many nodes etc) part of spark for calculating the recommendations for 500,000 items. This items include products for day care etc. Other software stack suggestions would also be very useful.It has to run on multiple vendor machines. Please suggest. Thanks shashi
Re: Software stack for Recommendation engine with spark mlib
As Sean says, precomputing recommendations is pretty inefficient. Though with 500k items its easy to get all the item vectors in memory so pre-computing is not too bad. Still, since you plan to serve these via a REST service anyway, computing on demand via a serving layer such as Oryx or PredictionIO (or the newly open sourced Seldon.io) is a good option. You can also cache the recommendations quite aggressively - once you compute a user or item top-K list, just stick the result in mem cache / redis / whatever and evict it when you recompute your offline model, or every hour or whatever. — Sent from Mailbox On Sun, Mar 15, 2015 at 3:03 PM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Thanks Sean, your suggestions and the links provided are just what I needed to start off with. On Sun, Mar 15, 2015 at 6:16 PM, Sean Owen so...@cloudera.com wrote: I think you're assuming that you will pre-compute recommendations and store them in Mongo. That's one way to go, with certain tradeoffs. You can precompute offline easily, and serve results at large scale easily, but, you are forced to precompute everything -- lots of wasted effort, not completely up to date. The front-end part of the stack looks right. Spark would do the model building; you'd have to write a process to score recommendations and store the result. Mahout is the same thing, really. 500K items isn't all that large. Your requirements aren't driven just by items though. Number of users and latent features matter too. It matters how often you want to build the model too. I'm guessing you would get away with a handful of modern machines for a problem this size. In a way what you describe reminds me of Wibidata, since it built recommender-like solutions on top of data and results published to a NoSQL store. You might glance at the related OSS project Kiji (http://kiji.org/) for ideas about how to manage the schema. You should have a look at things like Nick's architecture for Graphflow, however it's more concerned with computing recommendation on the fly, and describes a shift from an architecture originally built around something like a NoSQL store: http://spark-summit.org/wp-content/uploads/2014/07/Using-Spark-and-Shark-to-Power-a-Realt-time-Recommendation-and-Customer-Intelligence-Platform-Nick-Pentreath.pdf This is also the kind of ground the oryx project is intended to cover, something I've worked on personally: https://github.com/OryxProject/oryx -- a layer on and around the core model building in Spark + Spark Streaming to provide a whole recommender (for example), down to the REST API. On Sun, Mar 15, 2015 at 10:45 AM, Shashidhar Rao raoshashidhar...@gmail.com wrote: Hi, Can anyone who has developed recommendation engine suggest what could be the possible software stack for such an application. I am basically new to recommendation engine , I just found out Mahout and Spark Mlib which are available . I am thinking the below software stack. 1. The user is going to use Android app. 2. Rest Api sent to app server from the android app to get recommendations. 3. Spark Mlib core engine for recommendation engine 4. MongoDB database backend. I would like to know more on the cluster configuration( how many nodes etc) part of spark for calculating the recommendations for 500,000 items. This items include products for day care etc. Other software stack suggestions would also be very useful.It has to run on multiple vendor machines. Please suggest. Thanks shashi
