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Thanks my teammate @Ryan, we got 16th place. Thanks to H&M and Kaggle. This is a wonderful competition.

Framework

Framework

Data-splitting

We split the data into 3 groups: cg1(customer group 1) is the users with transactions in last 30 days; cg2(customer group 2) is the users without transactions in last 30 days, but with transactions in history. cg3(customer group 3) is the users without transactions in history.

  • cg1 and cg2: multi-recalls + rank.
  • cg3: popular items recall.

Recalls

  • popular items recall
  • repurchase recall
  • binaryNet recall
  • ItemCF recall
  • UserCF recall
  • W2V content recall
  • NLP content recall
  • Image content recall
  • Category content recall

Each recall method will recall 100 items for every user, then drop duplicates.

Rank

Feature Engineer

Item Feature

groupby article_id agg cols calculate statistics

  • cols: customer_id, price, sales_channel_id, and so on.
  • op: 'min', 'max', 'mean', 'std', 'median', 'sum', 'nunique'

User Feature

groupby customer_id agg cols calculate statistics

  • cols: price, article_id, sales_channel_id, and so on.
  • op: 'min', 'max', 'mean', 'std', 'median', 'sum', 'nunique'

Interaction Feature

  • count of user-item purchased in different window(1day, 3days, 1week, 2weeks, 1month).
  • The time-diff since the user last purchased the item

other features

  • ItemCF score
  • BinaryNet score

Model

  • lightgbm ranker
  • lightgbm binary

Ensemble

Ref to this link