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Our Experiments With Food Recommendations

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We want to talk all about Art & Science of Food Discovery @Swiggy. How we use advanced Machine Learning/AI on terabytes of data ( implicit/Explicit Feedback ) every day, to bring you recommendations that powers Restaurant Feeds, Filter Widgets, Personalized Collections.

We will also be talking about our Journey, Learning, and Challenges of building Food Recommendation System.

Published in: Technology
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Our Experiments With Food Recommendations

  1. 1. Our Experiments With Food Recommendations
  2. 2. 40K+ 35K+ M +
  3. 3. Home Feed Personalized Dish Collections Cross SellMenu Recommendations Restaurant First View Item First View
  4. 4. Understanding Customer Understanding Restaurants & Food Items Taste Profile Dish Cuisine Pure Veg/ Pure non-veg  Offers Popular Exclusive Brand CFT Ratings Healthy Taste Profile Veg/non-Veg Single/ Family Explorer Cravers Price Affinity Offer Affinity Weekend/Weekdays Home/Work Health Quotients Speed Brand centric Match Marking
  5. 5. Hyperlocal Serviceability Restaurant Stress Speed What it so Hard about these Recsys Accurate vs Diversity Discovery vs Repeat Freshness vs Stability Serendipitous vs Explainable
  6. 6. Data Interactions Orders Ratings Comment s Items Dish Family Cuisine Family Menus images
  7. 7. Collaborative Filtering ( Implicit data, MF methods) R1 R2 R3 - - - Rn C1 10 30 ? 89 10 C2 ? ? ? 9 30 - - 15 40 ? ? Cn ? ? ? 11 56  Order from Restaurant.  Ordered Dish, Cuisine, Category, Collection, Widgets, Tags  Menu Visits to the Restaurant  Visit on the collection/Widgets  Cart addition from Restaurant  Cart addition of an item.  Search Restaurant  Search Cuisine, Dish, Item etc  Cart clear on items.  Time spent on menu.  Time spent on cart. Cost Restaurant/ Dish / Cuisine / Tags
  8. 8. Content based People Generally like similar Items Recommend similar restaurant/Items Similarity ? Taste Profile/ Cost / Brand …
  9. 9. Topic visualization Demo
  10. 10. Truffle KFC Mac D 0.87 Burge r King Biggie s Burge r 'n' More Bundar 0.89 0.84 0.85 Cafe Thulp 0.92 0.84 0.89 Leon Grill 0.72
  11. 11. Understanding Food Catalog Machine learning models • SVM's • Logistic Regression • Word embedding Text • item name • Descriptions • Recipes • ingredients • reviews • Rest driven Cat, sub- cat, Images • Item image, • Restaurants image Dish Cuisine Category Veg/Non-veg Healthy/Non-Healthy Spicy/ calories
  12. 12. 12 'penne’, 'spaghetti’, 'macaroni’, 'ravioli', 'fusilli’, 'bechamel’, 'lasagne’ 'arabiatta', 'arrabbiata’ ‘alfredo’, 'pomodoro’, 'lasagna', 'fettuccine’ ‘pesto’ 'risotto', 'fettuccini’, model.most_similar(["pasta”]) model.similarity('chapatti', 'chapati') 0.89
  13. 13. Journey Collaborativ e Filtering ( Implicit feedback ) Content Based Hybrid Learning to Rank ( Content/ Customer information DNN Based methods ( Embedding, DCF, RNN)
  14. 14. Evolution of Recommendation Systems @Swiggy Home Feed Dish Discovery ( Personalized Collections) Page Generation Page Generation Real Time context
  15. 15. Data Gang

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