电商用户行为与商品数据分析数据集
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电商用户行为与商品数据分析数据集_E_commerce_User_Behavior_and_Product_Data_Analysis 数据来源：互联网公开数据 标签：电商, 用户行为, 商品分析, 购物篮分析, 推荐系统, 会话分析, 市场营销, 数据挖掘 数据概述： 该数据集包含来自电商平台的用户行为数据，记录了用户在平台上的浏览、购买等交互行为以及商品信息。主要特征如下： 时间跨度：数据记录的时间范围为一周。 地理范围：数据未明确标注地理位置，但可推测为电商平台的用户行为数据。 数据维度： brand_mapping.csv：品牌映射表，包含品牌ID与品牌名称的对应关系。 category_mapping.csv：商品类别映射表，包含类别代码与类别ID的对应关系。 dataset_1week.csv: 数据集中未包含，推测为原始数据。 session_1week.csv：用户会话数据，包括用户会话ID、用户ID、商品ID列表、商品数量、类别ID列表、首次发生时间、首次发生时间戳、星期几正弦值列表、星期几余弦值列表、事件最近程度列表、事件类型列表、品牌列表、价格列表、相对价格列表、日期索引等。 user_1week.csv：用户数据，包括用户ID、商品ID列表、商品数量、类别ID列表、首次发生时间、首次发生时间戳、星期几正弦值列表、星期几余弦值列表、事件最近程度列表、事件类型列表、品牌列表、价格列表、相对价格列表、日期索引等。 数据格式：数据以CSV格式提供，包含多个文件，便于数据处理和分析。 来源信息：数据来源于电商平台用户行为记录，已进行匿名化处理和初步整理。 该数据集适合用于用户行为分析、商品推荐、市场营销策略研究等领域。 数据用途概述： 该数据集具有广泛的应用潜力，特别适用于以下场景： 研究与分析：适用于电商用户行为分析、购物篮分析、用户画像构建等方面的学术研究。 行业应用：为电商平台、市场营销公司提供数据支持，尤其在个性化推荐、用户行为预测、市场趋势分析等方面具有实用价值。 决策支持：支持电商平台的运营决策，如优化商品推荐算法、调整营销策略、提升用户体验等。 教育和培训：作为数据挖掘、机器学习、推荐系统等课程的实训素材，帮助学生和研究人员深入理解用户行为分析方法。 此数据集特别适合用于探索用户购物行为模式、商品关联关系，并构建推荐模型，从而提升用户转化率和平台销售额。
Dataset Name: E-commerce User Behavior and Product Data Analysis Dataset Data Source: Publicly available data from the Internet Tags: e-commerce, user behavior, product analysis, market basket analysis, recommendation system, session analysis, marketing, data mining Data Overview: This dataset contains user behavior data from e-commerce platforms, recording interactive behaviors such as browsing and purchasing of users on the platform, as well as product information. The main features are as follows: 1. Time span: The time range of the data records is one week. 2. Geographic scope: The data does not clearly specify the geographic location, but it can be inferred as user behavior data of e-commerce platforms. 3. Data dimensions: - brand_mapping.csv: Brand mapping table, which contains the correspondence between brand IDs and brand names. - category_mapping.csv: Product category mapping table, which contains the correspondence between category codes and category IDs. - dataset_1week.csv: Not included in this dataset, presumed to be the original raw data. - session_1week.csv: User session data, including user session ID, user ID, product ID list, product quantity, category ID list, first occurrence time, first occurrence timestamp, weekday sine value list, weekday cosine value list, event recency list, event type list, brand list, price list, relative price list, date index, etc. - user_1week.csv: User data, including user ID, product ID list, product quantity, category ID list, first occurrence time, first occurrence timestamp, weekday sine value list, weekday cosine value list, event recency list, event type list, brand list, price list, relative price list, date index, etc. Data Format: The data is provided in CSV format with multiple files, facilitating data processing and analysis. Source Information: The data is derived from e-commerce platform user behavior records, and has been anonymized and preliminarily organized. This dataset is suitable for fields including user behavior analysis, product recommendation, marketing strategy research, and more. Data Usage Overview: This dataset has broad application potential and is particularly suitable for the following scenarios: 1. Research and analysis: Applicable to academic research on e-commerce user behavior analysis, market basket analysis, user portrait construction, and other related topics. 2. Industry applications: Provide data support for e-commerce platforms and marketing companies, with practical value in aspects such as personalized recommendation, user behavior prediction, and market trend analysis. 3. Decision support: Support the operational decision-making of e-commerce platforms, such as optimizing product recommendation algorithms, adjusting marketing strategies, and enhancing user experience. 4. Education and training: Serve as training materials for courses such as data mining, machine learning, and recommendation systems, helping students and researchers gain an in-depth understanding of user behavior analysis methods. This dataset is particularly suitable for exploring user shopping behavior patterns and product association relationships, as well as building recommendation models, thereby improving user conversion rates and platform sales.
