淘宝用户购物行为数据可视化分析

2014年是阿里巴巴集团移动电商业务快速发展的一年，例如2014双11大促中移动端成交占比达到42.6%，超过240亿元。相比PC时代，移动端网络的访问是随时随地的，具有更丰富的场景数据，比如用户的位置信息、用户访问的时间规律等。

“阿里移动推荐算法挑战赛”以阿里巴巴移动电商平台的真实用户-商品行为数据为基础，同时提供移动时代特有的位置信息，目标是让研究人员能够挖掘数据背后丰富的内涵，为移动用户在合适的时间、合适的地点精准推荐合适的内容。

本次可视化分析的目的是针对脱敏过的用户行为数据（包括浏览、收藏、加购和购买4类数据）进行分析，使用Python、Numpy、Pandas和Matplotlib工具完成可视化分析，帮助选手更好的理解数据，并作出商业洞察。
本次分析数据提供了1万用户量级的完整行为数据：user_action.csv，为了简化问题相比原数据集，我们去掉了user_geohash这个大部分情况为空的字段。

字段 字段说明 提取说明
user_id 用户标识 抽样&字段脱敏
item_id 商品标识 字段脱敏
behavior_type 用户对商品的行为类型 包括浏览、收藏、加购物车、购买，对应取值分别是1、2、3、4。
item_category 商品分类标识 字段脱敏
time 行为时间 精确到小时级别

2014 witnessed rapid growth in Alibaba Group's mobile e-commerce business. For example, during the 2014 Double 11 Global Shopping Festival, the share of mobile transactions reached 42.6%, exceeding 24 billion yuan. Compared with the PC era, mobile internet access is available anytime and anywhere, with richer contextual data such as user location information and time patterns of user access.

The "Alibaba Mobile Recommendation Algorithm Challenge" is based on real user-item interaction data from Alibaba's mobile e-commerce platform, and also provides location-specific data unique to the mobile era. The goal of the challenge is to enable researchers to uncover rich insights hidden behind the data, so as to accurately recommend appropriate content to mobile users at the right time and place.

The purpose of this visual analysis is to analyze anonymized user behavior data (including four categories: browsing, favoriting, cart addition, and purchase), and complete the visual analysis using Python, Numpy, Pandas and Matplotlib tools, to help participants better understand the data and derive business insights.

This analysis dataset provides complete behavior data on a 10,000-user scale: user_action.csv. To simplify the problem, compared with the original dataset, we removed the user_geohash field, which is mostly empty.

| Field | Field Description | Extraction Note |
| ---- | ---- | ---- |
| user_id | User identifier | Sampled and anonymized |
| item_id | Item identifier | Anonymized |
| behavior_type | User's behavior type towards items | Covers browsing, favoriting, cart addition, and purchase, with corresponding values 1, 2, 3, 4 respectively |
| item_category | Item category identifier | Anonymized |
| time | Behavior timestamp | Precise to the hour level

搜集汇总

背景与挑战

背景概述

该数据集基于2014年阿里巴巴移动电商平台的真实用户行为数据，包含1万用户量级的浏览、收藏、加购物车和购买四类行为记录，旨在通过可视化分析帮助理解用户购物模式并支持商业决策。数据经过脱敏处理，去除了冗余字段，适合用于数据分析和机器学习研究。