Flow of Brickmaster 19 Feature Generation Candidate Generation Ranking Serving Recall Precision Billions of Features Thousands of Items Hundreds of Items
Recall Precision Scalability and Flexibility Feature Generation Candidate Generation Ranking Serving Enlarge User Population Rankers for BIZ Idea Billions of Features Thousands of Items Hundreds of Items Scenario Feature
Recall Precision Scalability and Flexibility Feature Generation Candidate Generation Ranking Serving Enlarge User Population Rankers for BIZ Idea Billions of Features Thousands of Items Hundreds of Items Scenario Feature
Feature Generation User, Product, Context and Scenario User Pro fi le › Age › Area › Gender User Browsing › Click › Like › Search › UTM Product Pro fi le › Age › Gender › Text › Shop › Category Product Price › Mean › Median › IQR › Price change Date Time › Festival event › Next EC event › Holiday Scenario › Clickout › Impression › Last clickout time › Last view time
Feature Generation User, Product, Context and Scenario User Pro fi le › Age › Area › Gender User Browsing › Click › Like › Search › UTM Product Pro fi le › Age › Gender › Text › Shop › Category Product Price › Mean › Median › IQR › Price change Date Time › Festival event › Next EC event › Holiday Scenario › Clickout › Impression › Last clickout time › Last view time Product Price › Mean › Median › IQR › Price change
Feature Generation User, Product, Context and Scenario User Pro fi le › Age › Area › Gender User Browsing › Click › Like › Search › UTM Product Pro fi le › Age › Gender › Text › Shop › Category Product Price › Mean › Median › IQR › Price change Date Time › Festival event › Next EC event › Holiday Scenario › Clickout › Impression › Last clickout time › Last view time Product Price › Mean › Median › IQR › Price change
Feature Generation User, Product, Context and Scenario User Pro fi le › Age › Area › Gender User Browsing › Click › Like › Search › UTM Product Pro fi le › Age › Gender › Text › Shop › Category Product Price › Mean › Median › IQR › Price change Date Time › Festival event › Next EC event › Holiday Scenario › Clickout › Impression › Last clickout time › Last view time Product Price › Mean › Median › IQR › Price change
Feature Generation User, Product, Context and Scenario User Pro fi le › Age › Area › Gender User Browsing › Click › Like › Search › UTM Product Pro fi le › Age › Gender › Text › Shop › Category Product Price › Mean › Median › IQR › Price change Date Time › Festival event › Next EC event › Holiday Scenario › Clickout › Impression › Last clickout time › Last view time Product Price › Mean › Median › IQR › Price change
Feature Generation User, Product, Context and Scenario User Pro fi le › Age › Area › Gender User Browsing › Click › Like › Search › UTM Product Pro fi le › Age › Gender › Text › Shop › Category Product Price › Mean › Median › IQR › Price change Date Time › Festival event › Next EC event › Holiday Scenario › Clickout › Impression › Last clickout time › Last view time Product Price › Mean › Median › IQR › Price change
Candidate Generation Feature Engineering User Product Feature Training Data Generation Processed Dataset Modeling Dataset DNN Model Training Candidate Generator
Matcher (LSH) Model Inference User & Product Id Mapping User Product Candidate User & Product Embedding Imputation Transformation Scaling Negative Sampling
Candidate Generation Feature Engineering User Product Feature Training Data Generation Processed Dataset Modeling Dataset DNN Model Training Candidate Generator
Matcher (LSH) Model Inference User & Product Id Mapping User Product Candidate User & Product Embedding Imputation Transformation Scaling Negative Sampling
Candidate Generation Feature Engineering User Product Feature Training Data Generation Processed Dataset Modeling Dataset DNN Model Training Candidate Generator
Matcher (LSH) Model Inference User & Product Id Mapping User Product Candidate User & Product Embedding Imputation Transformation Scaling Negative Sampling
Candidate Generation Feature Engineering User Product Feature Training Data Generation Processed Dataset Modeling Dataset DNN Model Training Candidate Generator
Matcher (LSH) Model Inference User & Product Id Mapping User Product Candidate User & Product Embedding Imputation Transformation Scaling Negative Sampling
Candidate Generation Feature Engineering User Product Feature Training Data Generation Processed Dataset Modeling Dataset DNN Model Training Candidate Generator
Matcher (LSH) Model Inference User & Product Id Mapping User Product Candidate User & Product Embedding Imputation Transformation Scaling Negative Sampling
Ranking Feature Engineering User Product Candidate Training Data Generation Processed Dataset Modeling Dataset Scenario A Feature DNN Model Training Ranker A Model Inference Personalized Ranking A Imputation Transformation Scaling Negative Sampling
Ranking Feature Engineering User Product Candidate Training Data Generation Processed Dataset Modeling Dataset Scenario A Feature DNN Model Training Ranker A Model Inference Personalized Ranking A Imputation Transformation Scaling Negative Sampling
Ranking Feature Engineering User Product Candidate Training Data Generation Processed Dataset Modeling Dataset Scenario A Feature DNN Model Training Ranker A Model Inference Personalized Ranking A Imputation Transformation Scaling Negative Sampling
Health Checking Original DataSet Transformed Dataset Check DAG Success Check Data Splitting Training Data Generation Feature Engineering Imputation Outlier Detection Noti fi cation
Health Checking Original DataSet Transformed Dataset Check DAG Success Check Data Splitting Training Data Generation Feature Engineering Imputation Outlier Detection Noti fi cation Pipeline Level
Health Checking Original DataSet Transformed Dataset Check DAG Success Check Data Splitting Training Data Generation Feature Engineering Imputation Outlier Detection Noti fi cation Data Level
Personalized Recommendation › Match business goal (e.g. clickout, GMV) › We use proximal concept to solve business problems Business Goal Science Clickout User Engagement Min(Next Clickout Time) Max(Session Duration)
Personalized Recommendation › Match business goal (e.g. clickout, GMV) › We use proximal concept to solve business problems Business Goal Science Clickout User Engagement Min(Next Clickout Time) Max(Session Duration)
line_developers_tw
akki_megane
orgachem
kako351
jkebertz
sakaik
tbaldauf
kubukoz
safie_recruit
abenben
sumiren
bitkey
korodroid
sugarenia
chrislema
rasmusluckow
cassininazir
bkeepers
jeffersonlam
brianwarren
samlambert
swwweet
holman
jonrohan
kneath