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H&M Personalized Fashion Recommendations
Main process
Most popular
Most Popular items by
segment(all, age, etc..)
in the last 5 days
① Generate Item Candidates
LGBM Ranker
② Create Features ⑤ Post Processing
Copyright 2022 @kuto_bopro
Candidates Strategies Features
③ Learn Ranking Model ④ Ensemble
・・・
・・・
to cold start customers
Use age based most popular
items as predictions
to all customers
Remove the low offline sales
ratio items from the customers
who prefer offline.
Blend 6 model predictions
which is created by the different
candidates and features
Create below features by week,
and merge to the generated
candidates dataset.
Dataset & CV Strategy
train data week valid data week
create candidates and features week
104w
103w
102w
101w
100w
0w
Generate item candidates for each customers
and each week with multiple strategies.
transactions week
User based CF
Top similar items by
user based CF and
LightGCN
Different color
Different color items
from past purchased
Past purchased
Past purchased items
Item based CF
Top similar items of
past purchased items
by item based CF
Team: ZKMRD
CF: Collaborative Filtering
Best single model
CV: 0.0390 (public) LB: 0.0325
Key points about dataset
・Use only candidate examples generated, not all positive examples
・Use customers with at least one positive example in the candidates
・Remove not for sales now items from candidates
CF features
・score by item based CF
・score/rank by LightGCN (GCN based CF model)
Article dynamic attributes
・trend value
・weekly popular ranking
・purchase count (1day, 2day ago, last week..)
・purchase count by segment (age, item group)
・popular rank by segment (age, item group)
・mean sales channel
Article static attributes
・basic attributes in articles.csv
・Bert sentence vector
Customer dynamic attributes
・purchase count
・last purchase flag
・Days/Week since last purchase
・price/discount of purchased items
・mean sales channel
・purchase rate by item segment
・repurchase rate
Customer static attributes
・basic attributes in customers.csv
Private LB: 0.0329 (23th)