Hybrid recommender in spark

Question

I am trying to build a hybrid recommender using prediction.io which functions as a layer on top of spark/mllib under the hood.

I'm looking for a way to incorporate a boost based on tags in the ALS algorithm when doing a recommendation request.

Using content information to improve collaborative filtering seems like such a usual path although I cannot find any documentation on combining a collaborative algorithm (eg ALS) with a content based measure.

Any examples or documentation on incorporating content similarity with collaborative filtering for either mllib (spark) or mahout (hadoop) would be greatly appreciated.

Answer 1

This PredictionIO Template uses Mahout's Spark version of Correlators so it can make use of multiple actions to recommend to users or find similar items. It allows you to include multiple categorical tag-like content to boost or filter recs.

http://templates.prediction.io/PredictionIO/template-scala-parallel-universal-recommendation

The v0.2.0 branch also has date range filtering and popular item backfill is in development.