Recommendation engine for procurement system

1. RECOMMENDER
ENGINE FOR
PROCUREMENT
SYSTEM

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

3. 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

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

5. NUMBER OF TENDERS PER DAY
DATA EXPLORATION
2016 2019

6. CHALLENGES AND PITFALLS
METRICS DEFINITION
FILTERS
NOISY EMAILS
BOTS

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

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

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

10. high accuracy metric
good ranking metric
CHALLENGES AND PITFALLS
METRICS DEFINITION

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

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

13. TRAIN/TEST SPLIT
TRAIN TEST
TRAIN

14. TRAIN/TEST SPLIT
TRAIN TEST
TRAIN

15. TRAIN/TEST SPLIT
TEST USERS TRAIN TENDERS

16. FRAMEWORKS
Implicit

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

18. METRICS

19. METRICS

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

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

22. THANK YOU