Caterpillar Cowboy

dfdeshom:

1. How much data do I need? Given your data, you can use cross-validation or A/B testing to measure objectively the effectiveness of a recommender system.

2. We have this system in place, how do we know whether it is sane? See previous question.

3. My online recommender system is slow! Laziness is your friend: don’t recompute the recommendations each time you have new data.

4. My customers don’t like the recommendations!

   • Keep expectations in check: recommending products is difficult and even human beings have trouble doing it,
   • Explain the recommendations: nobody trusts a black box,
   • Allow your users to freely explore your data and products in convenient and exciting ways.

5. Which algorithm is best? You should start with simple algorithms: it worked well enough for Amazon. To do better, a mix of different algorithms is probably best. You can combine them using ensemble methods.

I’ll add one more: whenever possible, create similarity relationships between static objects, not user preferences. That way you can more easily solve the cold-start problem; you won’t need a training period where a user is forced to answer a dozen questions before getting useful results.