Download as PDF, PPTX
Uploaded byRenan Moreira de Oliveira
RecSys 2020 - iFood recommendation
iFood uses recommendation systems and machine learning models to improve the food delivery experience for customers. They generate word embeddings from text data to understand relationships between food items. A knowledge graph connects entities like users, restaurants, dishes and ingredients to enrich data. Graph convolutional networks are used to create embeddings that represent nodes based on their connections in the graph. These embeddings are used in recommendation systems at iFood to provide personalized search results, lists of similar items, and improve other machine learning models.
More Related Content
![[Product Camp 2020] - O que aprendemos sobre Testes A/B em um ano de experime...](https://cdn.slidesharecdn.com/ss_thumbnails/w-201202210553-thumbnail.jpg?width=640&height=640&fit=bounds)
Similar to RecSys 2020 - iFood recommendation
RecSys 2020 - iFood recommendation
- 1. How does iFooduse recommendation to improve food delivery experience ?! Luiz Mendes & Renan Oliveira
- 2. Luiz Mendes Lead DataScientist Food Graph Project From Belo Horizonte @lfmendes https://www.linkedin.com/in/lfomendes/
- 3. Renan Oliveira Principal Scientist Recommendation& Search From Rio @renan_oliveira
- 4. iFood Brain AI +DA + DE 150 people 40 squads 5 countries
- 5. IFOOD IN NUMBERS 40million orders 200k restaurants 150k drivers 1000 cities *these numbers are only from Brazil
- 6. 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 media: placing an order involves more money and logistical operation.
- 7. FOOD IS AVERY PERSONAL CHOICE Taste Profile Speed Brand Price Affinity Offer Affinity etc… Taste Profile Dish Cuisine Offers Rating etc… Match
- 8. CHALLENGES Locality geographical constraintfor 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
- 9. EVERYWHERE CAN BEPERSONALIZED Push Notification List of Restaurants List of Dishes Search Results UI Components
- 10. PILLARS OF IFOOD1:1 User Profile Which cuisine does she like most? Context How would the offer change on weekends? Journey If a user ordered "n" yesterday, what are the best "y" to offer today? Dish Profile Healthy? Low-calorie? How allergy-risk?
- 11. Candunga Collaborative Filtering for Lists 52 2 ? 3 4 5 3 ? ASH GOKU SEIYA
- 12. ALS WITH MULTIPLEMATRICES .9 .7 .5 .7 .5 .3 U1 U2 U3 Main Matrix Customized Discovery The classic ALS approach (user x item with implicit feedback) worked very well for recurring users. The conversion was not so good for users without a long history of purchases or for new items.
- 13. ALS WITH MULTIPLEMATRICES .35 .50 .28 .29 .61 .32 .42 .18 C1 C2 C3 "Clusters" Matrix Group of users Implicit and explicit feedback We can use any feature known to the user or the item to make a hybrid approach to content-based and collaborative filtering. Not using the user in the matrix index, the matrix becomes less sparse.
- 14. CANDUNGA .35 .32 .28 .29.61 .32 .42 .18 C1 C2 C3 .9 .7 .5 .7 .5 .3 U1 U2 U3 features - No limit on number of matrices - Features from user, item or booth - Added the feature layer - Items can be stores or products - Bayesian optimization to define the weight of each matrix - NDCG for offline evaluation
- 15. before after LUNCH LISTFOR LUIZ MENDES Japanese Healthy In the old version of the list, the top items were desserts and junk food. Luiz prefers japanese and healthy foods.
- 16.
- 17. RESULTS IN THEFEED OF ALL RESTAURANTES +38%conversion uplift +29%coverage uplift Recommendation Control
- 18.
- 19. EMBEDDINGS An embedding isa relatively low-dimensional space into which you can translate high-dimensional vectors. The most known embeddings are word embeddings, that are build based on the position of the word in a text and which words come before and after it. to learn more: https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture
- 20. EMBEDDINGS IN IFOOD food2vecsearch2vec*foodgraph Text based - fasttext Build with ifood text data Text based - word2vec Created based on search queries and the chosen restaurant Content and graph based Created based on connections and features
- 21.
- 22. food2vec Create word embeddings Buildwith ifood text data Use fasttext to have better out of the vocabulary embeddings Text data is very food specific and contains misspellings
- 23.
- 24. pasta related words vegetarianrelated words pizza related words japanese cuisine related words food2vec
- 25. pasta related words vegetarianrelated words pizza related words japanese cuisine related words Edamame is ingredient common in both japanese and vegetarian dishes and it is the middle of the groups food2vec
- 26. food2vec Create dish embeddings Usethe weighted mean of the dish name and description words Weight is based on IDF (inverse document frequency)
- 27.
- 28. food2vec Can be usedto augment dish lists Frango grelhado. Acompanha cuscuz marroquino com alho poró e ervilhas e cenoura. Filé de Saint Peter grelhado. Acompanha purê de abóbora e legumes ao vapor (brócolis e vagem). Salada Caesar de salmão cru defumado Hamburguer vegano + acompanhamento + mini salada Risoto integral de ervilha com shimeji e tofu. Take an example list Find similar dishes for all example Merge all neighbours and sort by similarity
- 29.
- 30.
- 31.
- 32.
- 33.
- 34. Movie Rotten link: www.rotten... Imdblink: www.imdb… Liked by: 90% Description: An orpha.. The Land Before Time Don Bluth Directed by Lucasfilm Produced by Knowledge Graph
- 35. Python? Which Pythonare you looking for? Knowledge Graph
- 36. Food Delivery containsmany entities: Users, Restaurants, Dishes, Ingredients, Delivery, Vendors... Food Graph CONTAINS Subject Object Apple pie><CONTAINS><apple
- 37. User A RestaurantR Tomatoes Chickpea Lettuce ordered from contains Vegan is to from Driver D Food Graph
- 38. Data Enrichment Stream of “Enriched”data Taxonomy Normalized Dish Names Serving Size Meal Type Ingredients Weights, Volumes Tags ... FKG “Better” Entity Representations RecSys Marketing Search ... Fill in Missing information by Graph completion * 33.7% of dishes have no description. Food Graph
- 39.
- 40. “You are theaverage of the five people you spend the most time with.” jim rohn
- 41. Food Graph Embeddings PinSage- pinterest Must ● use node features (excludes node2vec for example) ● be able to create embeddings for unseen nodes ● be scalable (millions of nodes and possibly billions of edges) ● tested on production somewhere https://medium.com/pinterest-engineering/pinsage-a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems-88795a107f48
- 42.
- 43. Food Graph Embeddings A Localizedconvolution based on Random Walks ● Starting from A make random walks and get the most visited nodes as neighbours
- 44. Food Graph Embeddings A Localizedconvolution based on Random Walks ● Starting from A make random walks and get the most visited nodes as neighbours ● B, C and D - First Hop A B C F E D
- 45. Food Graph Embeddings Localizedconvolution based on Random Walks ● Starting from A make random walks and get the most visited nodes as neighbours ● B, C and D - First Hop ● For each neighbour repeat this ○ B -> A and C ○ C -> A, B, E, and F ○ D -> A A B C F E D
- 46. Food Graph Embeddings Localizedconvolution based on Random Walks ● Starting from A make random walks and get the most visited nodes as neighbours ● B, C and D - First Hop ● For each neighbour repeat this ○ B -> A and C ○ C -> A, B, E, and F ○ D -> A How many hops? Usually 2 A B C F E D
- 47. Food Graph Embeddings Localizedconvolution based on Random Walks ● Starting from A make random walks and get the most visited nodes as neighbours ● B, C and D - First Hop ● For each neighbour repeat this ○ B -> A and C ○ C -> A, B, E, and F ○ D -> A How many hops? Usually 2, if you go to far you might find Kevin Bacon*, or use almost the whole graph for one node A B C F E D * https://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon
- 48. Food Graph Embeddings AComputational Graph for new A
- 49. Food Graph Embeddings AC D B ComputationalGraph for new AC D B
- 50. Food Graph Embeddings ABC F E D A C A A B Computational Graph for new AC D B
- 51. Food Graph Embeddings ABC F E D A C A A BConvolve 1. Get the first representation of A and C 2. Pass through a convolution layer to create the second B representation a. send neighbors through a dense neural network and then apply a aggregator/pooling function (e.g., a element-wise mean or weighted sum, denoted as γ ) Ɣ C A B B Convolve
- 52. Food Graph Embeddings ABC F E D A C A A BConvolve 1. The weights are shared between “same step” convolutions Ɣ C A B B Convolve Convolve Convolve Convolve w1 w1 w2
- 53. Food Graph Embeddingsin iFood PinSage algorithm uses a homogeneous graph, that is, all nodes are the same type. To transpose this to iFood we created first a Bipartite Graph of users and dishes.
- 54. Food Graph Embeddingsin iFood PinSage algorithm uses a homogeneous graph, that is, all nodes are the same type. To transpose this to iFood we created first a Bipartite Graph of users and dishes.
- 55. Food Graph Embeddingsin iFood PinSage algorithm uses a homogeneous graph, that is, all nodes are the same type. To transpose this to iFood we created first a Bipartite Graph of users and dishes.
- 56. Food Graph Embeddingsin iFood PinSage algorithm uses a homogeneous graph, that is, all nodes are the same type. To transpose this to iFood we created first a Bipartite Graph of users and dishes.
- 57. Food Graph Embeddingsin iFood PinSage algorithm uses a homogeneous graph, that is, all nodes are the same type. To transpose this to iFood we created first a Bipartite Graph of users and dishes.
- 58. Food Graph Embeddingsin iFood We use food2vec embeddings, category, cuisine and many other features as input. The result is an embedding that contains both content from itself and neighbours.
- 59.
- 60. Embeddings in Recommendation Theseembeddings can e will be used in many projects in iFood: ● Similarity based recommendation ● Improve search results ● As features for classification/regression models ● Features for recommendation algorithms ● Improve Lean to Rank models
- 61.
- 62. THANK YOU! if youwant to revolutionize the food delivery market we're hiring! https://materiais.ifood.com.br/recsys