H&M 23th place solution

kaggleのH&Mコンペの23位になりました。その解法の概略図です。

コンペURL: https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/
解法のdiscussion: https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/discussion/324085

Transcript

1. 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)
   

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