First, I’m not sure this is a good idea. You loose the realtime nature of recommendations based on the up-to-the-second recording of user behavior. You get this with live user event input even without re-calculating the model in realtime.
Second, no you can’t disable queries for user history, it is the single most important key to personalized recommendations. I’d have to know more about your application but the first line of cost cutting for us in custom installations (I work for ActionML the maintainer of the UR Template) is to make the Spark cluster temporary since it is not needed to serve queries and only needs to run during training. We start it up, train. then shut it down. If you really want to shut the entire system down and don’t want realtime user behavior you can query for all users and put the results in your DB or in-memory cache like a hashmap, then just serve from your db or in-memory cache. This takes you back to the days of the old Mahout Mapreduce recommenders (pre 2014) but maybe it fits your app. If you are doing E-Commerce think about a user’s shopping behavior. They shop, browse, then buy. Once they buy that old shopping behavior is no longer indicative of realtime intent. If you miss using that behavior you may miss the shopping session altogether. But again, your needs may vary. On Jun 1, 2017, at 6:19 AM, Martin Fernandez <[email protected]> wrote: Hello guys, we are trying to deploy Universal Recommender + predictionIO in our infrastructure but we don't want to distribute hbase accross datacenters cause of the latency. So the idea is to build and train the engine offline and then copy the model and ealstic data to PIO replicas. I noticed when I deploy engine, it always tries to connect to HBASE server since it is used to query user history. Is there any way to disable those user history queries and avoid connection to HBASE? Thanks Martin
