智慧旅游场景游客画像数据

依托个推自身庞大的移动互联网综合服务能力和积累的大数据基础、深入的大数据分析洞察水平。通过对 数据的处理、分析以及深入挖掘，研发建成了完整的全域旅游大数据指标体系和文旅数据输出能力。为全域旅游和智慧城市等领域提供大数据解决方案。在自研的每日治数平台上对数据抽取、清理和处理，完成数据仓库层建设，通过用户线下场景偏好，通过机器学习模型进行建模，加工成智慧旅游场景游客画像数据，从时空维度（景区、时间）刻画用户旅游行为画像。
一、数据抽取、清理和处理
数据抽取：从数据库中抽取与用户LBS类相关的原始数据。
数据清理：对抽取的数据进行清洗，去除重复、错误或无关的信息。
数据处理：对清洗后的数据进行必要的转换和整合。
二、数据仓库层建设
1.数据模型设计
2.ETL过程
3.数据仓库优化
三、基于LBS类的用户兴趣偏好建模
特征提取：从用户LBS类数据中提取关键特征，如景区相关的LBS到访线下场景分类（如世界遗产、国家级景点、海滩、省级景点、纪念馆、观景点、公园广场、公园、公园内部设施、动物园、城市广场、植物园、水族馆）、具体时间、到访频率、到访时长等
模型选择：根据问题的性质和数据的特点，选择合适的机器学习模型进行建模。对于用户未来到访兴趣偏好这类问题，可以考虑使用时间序列模型模型（如AR、MA、ARIMA等）。
生成旅游偏好标签：将训练好的模型应用于预测新用户或新数据，生成用户的旅游偏好标签。

Leveraging Getui's comprehensive mobile internet service capabilities, accumulated big data foundation, and in-depth big data analysis and insight capabilities, Getui has developed and established a complete all-for-one tourism big data indicator system and culture and tourism data output capabilities through data processing, analysis and in-depth mining, providing big data solutions for fields including all-for-one tourism and smart cities. On its self-developed Daily Data Governance Platform, data extraction, cleaning and processing are performed to complete the data warehouse layer construction. Based on users' offline scene preferences, modeling is conducted via machine learning models to generate tourist profile data for smart tourism scenarios, which depicts users' tourism behavior profiles from spatiotemporal dimensions (scenic spots and time).

1. Data Extraction, Cleaning and Processing
- Data Extraction: Extract raw data related to user location-based services (LBS) from databases.
- Data Cleaning: Clean the extracted data by removing duplicate, erroneous or irrelevant information.
- Data Processing: Perform necessary conversions and integrations on the cleaned data.

2. Data Warehouse Layer Construction
1. Data Model Design
2. ETL Process
3. Data Warehouse Optimization

3. User Interest Preference Modeling Based on LBS Data
- Feature Extraction: Extract key features from user LBS data, including classified offline visit scenarios related to scenic spots (e.g., World Heritage Sites, National-level Scenic Spots, Beaches, Provincial-level Scenic Spots, Memorial Halls, Viewing Spots, Park Squares, Parks, Park Internal Facilities, Zoos, Urban Squares, Botanical Gardens, Aquariums), specific time, visit frequency, visit duration, etc.
- Model Selection: Select appropriate machine learning models for modeling based on the nature of the problem and the characteristics of the data. For issues such as users' future visit interest preferences, time series models (e.g., AutoRegressive (AR), Moving Average (MA), AutoRegressive Integrated Moving Average (ARIMA), etc.) can be considered.
- Tourism Preference Label Generation: Apply the trained model to predict new users or new data, and generate users' tourism preference labels.

搜集汇总

数据集介绍

特点

智慧旅游场景游客画像数据是一个包含多维度游客信息的数据集，规模为4001条，每日更新，主要用于全域旅游和智慧城市的大数据解决方案。