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iFood Recommendations

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Presentation made at the Latin America School on Recommendation System (LARS). This presentation presents the Food Delivery recommendation case.

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iFood Recommendations

  1. 1.  Do you know that feeling when an app knows exactly what you want? @renan_oliveira Principal Data Scientist
  2. 2. AN UNIQUE MOMENT - YOU CAN'T FAST-FORWARD OR SKIP Time and Quality Decision process Special occasion We are not a streaming service. Ordering food has an additional difficulty being assertive because fixing a mistake is not like skipping a song: placing an order involves more money and logistical operation.
  3. 3. IFOOD IN NUMBERS 20 million orders 270k drivers 100k restaurants 660 cities https://www.uol.com.br/tilt/noticias/redacao/2019/08/29/haja-fome-ifood-recebe-mais-de-7-pedidos-a-cada-segundo-no-brasil.htm
  4. 4. FOOD IS A VERY PERSONAL CHOICE Taste Profile Speed Brand Price Affinity Offer Affinity etc… Taste Profile Dish Cuisine Offers Rating etc… Match
  5. 5. CHALLENGES Locality geographical constraint for model training Speed if you are hungry you will want to eat soon Serviceability production capacity and restaurant quality Feedback implicit (engagement) vs explicit (ratings) Growth cold start problem
  6. 6. DEPLOY MODEL PIPELINE
  7. 7. COLLABORATIVE FILTERING - IMPLICIT FEEDBACK 5 2 2 ? 3 4 5 3 ? ASH GOKU SEIYA Orders User Journey Built engagement ALS is very good to reinforce tastes but fails to identify new tastes. Since our problem has geographical constraints, our data is more sparse than usual.
  8. 8. USER HISTORY + CONTENT BASED BOUGHT TOMATO PIZZA - 3 FREE DELIVERY - 2 TOMATO PIZZA - 2 FAST DELIVERY - 1 TRENDING - 2 HAMBURGUER - 4 COKE - 2 FAST DELIVERY - 1 TERMS AND RELEVANCE TOMATO PIZZA - 5 HAMBURGUER - 4 FAST DELIVERY - 4 COKE - 2 FREE DELIVERY - 2 FAST DELIVERY - 3 TRENDING - 2 HISTORY TOMATO PIZZA FREE DELIVERY COKE SALAD JUICE FAST DELIVERY TOMATO PIZZA HAMBURGUER FAST DELIVERY TERMS STORESRECSYS
  9. 9. “AKINATOR” - EXPLICIT FEEDBACK 1. Select cuisines 2. Select the expected price 3. Select time range Kmeans is great for a cold start scenario in which we don't know much about the customer but we have information about other costumers and restaurants nearby.
  10. 10. renanoliveira.net THANK YOU! if you want to revolutionize the food delivery market we are hiring! www.ifood.com.br/carreiras

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