Anonymized Product Data

This dataset contains <b>anonymized train and test data used for reproducing results on product score prediction</b> based on both <b>product attributes and images</b>. It is specifically used for evaluating the performance of the<b> </b>model, which predicts the selling potential or conversion of new listings. The dataset is designed for benchmarking and validation purposes in e-commerce ranking and recommendation systems. The product attributes have been anonymized by replacing actual values with generic identifiers (e.g., fabric → fabric_1, fabric_2; brand → brand_1, brand_2), ensuring privacy while retaining the statistical properties necessary for analysis.<b>File Details</b><b>Format:</b> Pickle (.pkl)<b>Contents:</b><b>Product attributes:</b> Structured metadata (e.g., type, price, fabric, etc.).<b>Image embeddings:</b> Precomputed feature vectors extracted from product images using deep learning models.<b>Product score (target variable):</b> Incorporates logged transformations of sales units, product page views (PPV), and impressions with a weighting parameter (α).<br><br>