If all recs come back score 0 then they are from the most popular items only, not from the Collaborative Filtering algorithm.
If you truly mean “all” then you have a problem in config or nearly 0 data. Don’t mess with tuning until you solve this. On Jun 21, 2017, at 2:27 PM, Daniel Gabrieli <[email protected]> wrote: Hi, Question about the Universal Recommendation Engine (UR). The challenge I am experiencing right now is that all my recommendations come back with a score of 0 (and are the same recommendations for all users). After training the engine, is there a way to get the metrics about the trained model? For example, which (if any) indicators have a meaningful impact on the recommendation? I would like to tune the parameters maxCorrelatorsPerItem and minLLR but I am not sure how to figure out how the default parameters differ from the trained models results. For example, what is the LLR for the indicators in the trained model currently? How many correlators are currently considered significant? Thanks!
