大模型训练场景手机品牌用户洞察数据

手机品牌用户洞察数据助力大模型训练，十亿级手机用户行为数据，经去标识化、聚合化处理后，可转化为该品牌宏观群体画像。这些高质量统计数据，能让大语言模型高效学习真实用户分布、社会常识与市场趋势，进而优化推理能力、校准输出并减少幻觉，是大模型训练的优质 “事实基准” 与 “认知图谱”，复用性强，适用于预训练、监督微调与效果评估。
其详尽统计维度可直接服务于大模型训练优化，核心应用有四方面。一是预训练与知识增强，年龄、性别等字段，为模型提供社会人口统计学与消费行为学知识，融入预训练语料能强化模型对现实用户群体的理解，使其回答商业相关问题时更贴合逻辑。二是优化对话与推荐能力，年龄占比、TGI 指标等将群体偏好量化，微调阶段可借此构建指令样本，让模型学习 “量化分析” 思维，提升垂直领域对话专业性。三是校准输出与反 “幻觉”，可作为基准检验模型输出真实性，如验证 “平价电商手机品牌用户是高净值人群” 这类描述，还能集成到 RAG 系统，确保模型回答准确。四是合成模拟对话数据，依托年龄、性别等用户特征标签，批量生成高质量模拟数据，扩充训练集，助力模型提升与不同用户互动的泛化能力。1、数据收集：
通过个推软件开发工具包对海量、离散的用户设备使用行为数据进行收集，进而加工产出以群体画像为核心的数据资产。
2、数据处理：
首先，进行隐私保护处理。 为确保数据无法关联到特定自然人。通过数据管道与处理引擎，对数据进行清洗、脱敏和聚合；
所有涉及用户标识的数据均会经过密码学哈希函数进行单向、不可逆的混淆计算。此过程实现了数据的匿名化与去标识化，从源头切断了信息回溯至特定个人的可能性。
其次，执行群体统计聚合。 在匿名化数据的基础上，系统按照预先设定的分析维度，对用户行为进行群体层面的统计汇总。此过程不关注个体行为，而是将个体行为汇聚成宏观的统计量，从而生成如“年龄分布”、“性别分布”等反映整体用户构成的数据集合。
3、算法加工：
引入机器学习模型进行标签预测:
对于无法直接获取的人口属性及深层兴趣偏好，本方案采用预设的机器学习模型进行推断。该模型首先以用户授权的、经脱敏后的跨平台全域手机品牌使用行为数据作为输入特征，通过算法计算，输出具有“18-25岁”等特定标签的潜在用户群体在全量用户中的预估分布比例，从而完成宏观层面的群体定义与基准量化。
在此基础上，我们将同一分析框架应用于具体场景，聚焦于单个特定目标手机品牌，通过相同的算法计算得出上述已定义的各类目标群体在该手机品牌用户中的分布比例，即群体渗透率。所有预测结果均以概率分布形式呈现，服务于群体洞察，而非对个体进行精准刻画。
同时通过计算TGI分步，来量化群体特征相对于总体的偏好强度，并将TGI指标作为洞察报告的关键维度之一。
以报告形式产出标准化数据资产。 首先，基于具体业务场景向大模型下达精准指令，生成包含固定框架、动态模块与数据占位符的标准报告模板，适配多场景使用需求；随后，通过定制化算法程序，将经清洗、脱敏、聚合与预测流程处理后的多源异构数据，转化为格式统一、类型匹配、精度合规的高质量标准化格式数据，无缝适配报告模板填充需求，将数据填入到报告模板中。

Mobile brand user insight data empowers Large Language Model (LLM) training. The billion-scale mobile user behavior data, after de-identification and aggregation processing, can be transformed into the macro-group portrait of the brand. These high-quality statistical data enable LLMs to efficiently learn real user distributions, social common sense and market trends, thereby optimizing reasoning capabilities, calibrating outputs and reducing hallucinations. They serve as high-quality "factual benchmarks" and "cognitive graphs" for LLM training, with strong reusability, suitable for pre-training, supervised fine-tuning and model evaluation.

Its detailed statistical dimensions can directly serve the optimization of LLM training, with four core application scenarios:
1. Pre-training and knowledge enhancement: Fields such as age and gender provide the model with demographic and consumer behavior knowledge. Integrating this data into pre-training corpora can enhance the model's understanding of real user groups, enabling it to answer commercial-related questions more logically.
2. Optimizing dialogue and recommendation capabilities: Indicators such as age proportion and TGI index quantify group preferences. In the fine-tuning stage, instruction samples can be constructed based on this data, allowing the model to learn "quantitative analysis" thinking and improve the professionalism of dialogue in vertical fields.
3. Calibrating outputs and countering hallucinations: It can serve as a benchmark to verify the authenticity of model outputs. For example, it can verify statements like "users of affordable e-commerce mobile phone brands are high-net-worth groups". It can also be integrated into Retrieval-Augmented Generation (RAG) systems to ensure the accuracy of model responses.
4. Synthesizing simulated dialogue data: Relying on user feature tags such as age and gender, high-quality simulated data can be generated in batches to expand the training set, helping the model improve its generalization ability when interacting with different users.

1. Data Collection:
Collect massive and discrete user device usage behavior data through the GeTui Software Development Kit (SDK), and then process and produce data assets centered on group portraits.

2. Data Processing:
First, privacy protection processing is carried out. To ensure that the data cannot be linked to specific natural persons, data cleaning, desensitization and aggregation are performed through data pipelines and processing engines; all data involving user identifiers will be subjected to one-way and irreversible obfuscation calculation via cryptographic hash functions. This process achieves data anonymization and de-identification, cutting off the possibility of tracing information back to specific individuals from the source.
Second, group statistical aggregation is performed. Based on the anonymized data, the system conducts statistical summary of user behaviors at the group level according to pre-set analysis dimensions. This process does not focus on individual behaviors, but aggregates individual behaviors into macroscopic statistical metrics, thereby generating data sets reflecting overall user composition, such as "age distribution" and "gender distribution".

3. Algorithm Processing:
Introduce machine learning models for label prediction:
For demographic attributes and deep-seated interest preferences that cannot be directly obtained, this solution uses pre-configured machine learning models for inference. The model first takes user-authorized and desensitized cross-platform full-domain mobile brand usage behavior data as input features, and through algorithmic calculation, outputs the estimated distribution proportion of potential user groups with specific tags such as "18-25 years old" among all users, thereby completing macro-level group definition and benchmark quantification.
On this basis, we apply the same analysis framework to specific scenarios, focusing on a single specific target mobile phone brand, and use the same algorithm to calculate the distribution proportion of the above-defined target groups among the users of this mobile phone brand, namely group penetration rate. All prediction results are presented in the form of probability distributions, serving group insights rather than accurately portraying individuals.
Meanwhile, the preference intensity of group characteristics relative to the overall population is quantified by calculating the TGI index, and the TGI index is taken as one of the key dimensions of insight reports.
Produce standardized data assets in the form of reports. First, issue precise instructions to the LLM based on specific business scenarios to generate standard report templates containing fixed frameworks, dynamic modules and data placeholders, adapting to multi-scenario usage needs; then, through customized algorithm programs, convert the multi-source heterogeneous data processed through cleaning, desensitization, aggregation and prediction processes into high-quality standardized format data with uniform format, matched type and compliant accuracy, which seamlessly fits the filling requirements of the report template, and fills the data into the report template.

搜集汇总

背景与挑战

背景概述

该数据集专注于手机品牌用户洞察，提供年龄、性别分布和TGI分析等统计信息，数据规模为500条并每月更新。它旨在支持大模型训练，通过去标识化、聚合化处理的高质量数据，优化模型的推理能力、校准输出并减少幻觉，适用于预训练、微调和效果评估等多种场景。数据集经过隐私保护处理，以宏观群体画像形式呈现，复用性强，可作为大模型训练的“事实基准”与“认知图谱”。