0

I want to implement a Universal Recommender for our site.

I've successfully set up the base configuration customized by the tutorial.

The service we provide is a e-commerce, where users can buy, like, rate from 0 to 5 and save a product among the other actions.

I'm a little confused about how to set up these:

  • Like / unlike;
  • Save / remove;
  • Rate.

I know I can attach properties on the events, but I don't get how set a negative weight to the unlike and remove events. Also I don't know how the rating could be weighted by the algorithm, if I didn't set anywhere a range in which evaluate a product.

Hope you can enlighten me.

Bests

Marco Fedele
  • 2,090
  • 2
  • 25
  • 45

1 Answers1

2

The Universal Recommender is build on the Correlated Cross-Occurrence algorithm, which measures the correlation of any number of indicators with the action to be recommended. In your case you want a user to "buy" so you should include that as the primary event—the one by which all others are compared.

The primary event is

  • buy

So that makes the secondary events:

  • like
  • unlike
  • save (to shopping cart?)
  • remove (from shopping cart? if so not much of an indicator probably)
  • rate (is there a range?)

You could make some guesses about low rating means "hate" and high ratings mean "love", toss out middle/ambiguous ratings and replace rate with these 2 new events.

If you don't have "buy" you could substitute something like "love" or "like" in it's place as the primary event.

The point of separating the events into 2 types is that the Universal Recommender will test to see which of the secondary events correlate with "buy" and will automatically weight them so that all of them can be used for recommendations.

pferrel
  • 5,673
  • 5
  • 30
  • 41