Project 2019 06

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Lviv Data Science Summer School

Recommendation engine for procurement system

Data & Analytics

RECOMMENDER
ENGINE FOR
PROCUREMENT
SYSTEM

• Problem and task overview
• Data exploration
• Challenges
• Recommendation model
workflow
• Metrics
• Results
• Future Work
OUTLINE
TOPICS TO BE COVERED

TASK
TARGET
User can view
personalized recommendations of tenders
REQUIREMENTS
An executor applied on tender from the
proposed list
Help executors find relevant tenders
PROBLEM & TASK

EMPLOYEES TENDERS ROWS OF DATA
84K 1 471K690K
DATA EXPLORATION

NUMBER OF TENDERS PER DAY
DATA EXPLORATION
2016 2019

CHALLENGES AND PITFALLS
METRICS DEFINITION
FILTERS
NOISY EMAILS
BOTS

CHALLENGES AND PITFALLS
BOTS
MISLEADING DATA
➔47K visited and favorites tenders
per employee
➔300 employees have more than
1K interactions

➔different procurement qualifier
codes
➔multiple or absent delivery
regions per purchase
➔range of declared purchase
value
CHALLENGES AND PITFALLS
FILTERS

➔one employee with different companies
➔employee without information
CHALLENGES AND PITFALLS
NOISY EMAILS
difficult to recommend relevant
tenders

high accuracy metric
good ranking metric
CHALLENGES AND PITFALLS
METRICS DEFINITION

INTERACTIONS
FAVORITE NOTED VISITED PLANNED
APPLICATION
18.6% 13.8% 8.4% 2.5%

DATA FEATURES
STEP 1
APPLICATIONS
ONLY
STEP 2
APPLICATIONS +
OTHER INTERACTIONS
STEP 3
ALL INTERACTIONS +
OTHER FEATURES

TRAIN/TEST SPLIT
TEST USERS TRAIN TENDERS

MODELS
Im
Embedding Dot
Bias model
Alternating Least
Squares model
CatBoost Classifier
Custom collaborative
filtering model

• Autoencoders
• Filters
• Hyperparameters
optimisation
• NLP
WHAT’S NEXT?

TEAM
Alexandra Klinnikova
Anastasiia Dudko
Danylo Makovskyj
Maksym Tarnavsky
Nataliia Tkachenko
Victoria Lytvyn
Mentor
Hanna Pylieva

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