The hadoop recommenders are not the really state of the art anymore. We are recommending one based on the SimilarityAnalysis.cooccurrence methods and the spark-itemsimilarity driver if you wish. These in combination with a search engine produce an incredibly flexible recommender that ingests lots more data about the user and so can recommend in cases the old ones or even the new Spark ALS recommender can’t help with.
There is an OSS version here: https://github.com/PredictionIO/template-scala-parallel-universal-recommendation It works as a template or plugin to the prediction.io framework, which has all the event ingest/storage/train/serving built in. Check it out. On Aug 20, 2015, at 12:30 PM, Mike C <[email protected]> wrote: Hi! I've made a custom User Recommender using the Mahout API, using GenericUserBasedRecommender. I'm not quite sure how to take the next step to get it working across Hadoop. RecommenderJob appears to be for Item Recommenders and it's not really clear how to adapt it for a custom Recommender. Anyone have any pointers to how this can be done in my case? Thanks. Mike
