Recommender systems: optimizing least squares or user experience?
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Building a recommender system is a lot harder than just filling in a matrix with missing values. One has to deal with the constraints of modern web. People don't want to spend time and don't want to think. The recommendations don't only need to be relevant, but also divers and robust. I touch upon those hard to measure aspects with lessons learned from my recommender system www.namesilike.com
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Recommender systems: optimizing least squares or user experience?
- 1. Recommender systems Optimizing least squares or user experience? Kasper Van Lombeek 15 December 2018
- 2. Problem 1 Accuracy does not always give trust
- 3. 1. What do these names have in common? Wout Seppe Gust Senne Ward Nand Tuur Ferre Siebe
- 4. UK Flanders France Miel Bas Matz Gust Finley Bailey Toby Gabin Corentin Titouan 1. What do these names have in common?
- 5. Wout Seppe Gust Senne Ward Nand Tuur Ferre Siebe So if a user clicked on: We can recommend: Korneel Staf Kamiel 1. What do these names have in common?
- 6. Caroline Céline Stéphanie Virginie Sandrine Myriam Sonia Christelle Claire 2. What do these names have in common?
- 7. 2. What do these names have in common?
- 8. Caroline Céline Stéphanie Virginie Sandrine Myriam Sonia Christelle Claire So if a user clicked on: We can recommend: Valérie Aline Karine 2. What do these names have in common?
- 9. 3. What do these names have in common? Oscar Sem Gust Ilyas Milo Kamiel Owen Miel Remi
- 10. 3. What do these names have in common?
- 11. So if a user clicked on: We can recommend: Muhammed Luka Ibrahim 3. What do these names have in common? Oscar Sem Gust Ilyas Milo Kamiel Owen Miel Remi
- 13. Mathematically nothing MSE is still minimized What went wrong?
- 14. Recommendations need to be Relevant Diverse Serendipity Robust i.e. make the user feel like you understood him
- 15. Solution Early stage: Business rule If not clicked on a arabic name, do not predict one Later: Ensemble model Combine time series features with latent features
- 21. Problem 2 How do we get to know the user his taste?
- 23. Learn fast
- 24. 1. On what name will the user click? Wout (Flemish) Seppe (Flemish) Gust (Flemish) Ward (Flemish)
- 25. 1. On what name will the user click? How long does it took the user? - > Very long What did you learn? - > Nothing
- 26. 2. On what name will the user click? Kiandro (not popular) Malik (not popular) Twan (not popular) Leon (very popular)
- 27. 2. On what name will the user click? How long does it took the user? - > Not Long What did you learn? - > Nothing
- 28. Ideally 4 very different names 1 name that a user likes more than the 3 others
- 29. Solution
- 31. Problem 3 Who are we serving?
- 37. Feedback loop? 1. Start with the ambition to build a general bookshop 2. Your recommender system recommends mostly cook books 3. People who love cookbooks love your recommender system, they invite other friends 4. Your measurement of satisfaction of your recommender system starts to be biased because your users are mostly cook book lovers 5. You end up with a cook book shop
- 38. But why? The world is burning and I build a baby name recommender...
- 39. There is nothing important or of great scientific merit in this problem, they will sneer. These great minds should have been putting their immense brain power to something that would benefit mankind. Unfortunately, whatever non-scientists may think about science and its importance, my experience has been that most (?data-)scientists do their research because they are interested in the results and get intellectual excitement out of the work. Seldom do good scientists think about the eventual importance of their work.
- 40. Thank you Kasper Van Lombeek kvanlombeek