Retailrocket recommender system dataset

The dataset consists of three files: a file with behaviour data (events.csv), a file with item properties (item_properties.сsv) and a file, which describes category tree (category_tree.сsv). The data has been collected from a real-world ecommerce website. It is raw data, i.e. without any content transformations, however, all values are hashed due to confidential issues. The purpose of publishing is to motivate researches in the field of recommender systems with implicit feedback.

本数据集包含三个文件：行为数据文件（events.csv）、商品属性文件（item_properties.csv）以及类目树描述文件（category_tree.csv）。该数据采集自真实电子商务网站，属于未经过任何内容转换的原始数据；但出于保密需求，所有数值均已完成哈希处理。本数据集的发布旨在推动隐式反馈（implicit feedback）推荐系统领域的相关研究。

搜集汇总

背景与挑战

背景概述

Retailrocket推荐系统数据集包含电子商务网站的用户行为、商品属性和类别树数据，覆盖4.5个月周期，含270万次事件和140万独立访客。数据经过哈希处理，适用于基于隐式反馈的推荐算法研究，特别是针对会话型推荐和异常用户检测等场景。