I get ten to fifteen questions a week on recommender systems from entrepreneurs and engineers. Sometimes, I help people find their way in the literature. On occasion—for a consulting fee—I get my hands dirty and evaluate, design or code specific algorithms. But mostly, I answer the same questions again and again:
1. How much data do I need?
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?