alimamaTech/TRACE

We introduce TRACE, the first benchmark dataset for post-click GMV prediction with delayed feedback.

📁 Data structure includes:

1. Features:

   • User/Item/Contextual Attributes: 22 such attributes are provided (e.g., feature_0 to feature_21).
   • Click Timestamp: click_time.

2. Labels and Sequence Information:

   • Gross Merchandise Volume (GMV) Sequence: The sequence of transaction amounts for all purchases associated with a click. This is represented by dirpay_amt for the current purchase and prev_dirpay_amt for previous purchases.
   • Purchase Count Sequence: The sequence indicating the order of each purchase within the attribution window for a given click. count represents the current purchase's order, and total_counts indicates the final number of purchases.
   • Purchase Timestamp Sequence: The sequence of timestamps for each purchase. pay_time typically represents the timestamp of the current purchase, and prev_pay_time contains timestamps of previous purchases.
   • Repurchase Indicator (multi_tag): A binary label (0 or 1) indicating whether the GMV generated by a click is from a single purchase (0) or a repurchase (1). This is derived from the number of purchases.
   • Final Ground-Truth GMV Label: The total GMV accumulated by the end of the attribution window. This is calculated as the sum of dirpay_amt.