The following is a general method and steps, but in actual application, it is important to ensure that data collection and use fully comply with local privacy, data protection and anti-discrimination laws and regulations, and obtain the user's informed consent. The following is only shared as a technical idea: 1. Data collection and permission management • User behavior log: Collect female users' browsing history, search history, click data, shopping cart/order records, etc. from the application with explicit authorization. • Public evaluation and feedback: Collect user comments, ratings and other information on products to extract preferences for product attributes. • Third-party data or questionnaire survey: Under the premise of complying with the privacy policy and user consent, obtain more user preference data through questionnaires and surveys. 2. Data preprocessing and feature construction • Data cleaning: Process missing values, outliers, and duplicate data to ensure data quality. • User portrait construction: Count and classify users' historical behavior data, consumption amount, frequency, browsing time, category preferences, etc. • Feature extraction: Use natural language processing (NLP) technology to analyze comments and search terms, extract keywords, and construct semantic features. In addition, use statistical methods to extract time series features or consumption patterns. 3. User segmentation and preference modeling • Clustering algorithm: K-means, hierarchical clustering or other unsupervised learning methods can be used to cluster users according to shopping behavior, visit frequency, consumption category, etc., to outline different preference groups. • Collaborative filtering: With the help of collaborative filtering algorithms, implicit preferences between similar user groups can be mined to predict the products that individual users may be interested in. • Classification and recommendation model: Using supervised learning (such as random forest, gradient boosting tree, deep learning model, etc.), a model is built to predict the user's interest in specific categories or products, forming a personalized recommendation algorithm. 4. Advertising optimization • Precise population positioning: Based on clustering results and prediction models, female users are segmented into multiple preference groups, and targeted advertising content is formulated for each group. • Dynamic adjustment: Through A/B testing and online learning, the advertising effect is evaluated in real time, and the model and segmentation strategy are iteratively optimized according to conversion rate and interactive feedback. • Multi-dimensional indicators: In addition to gender labels, multi-dimensional data such as region, age, and consumption ability are combined to avoid single-dimensional stereotypes and make advertising more in line with individual diversity. 5. Ethical and regulatory considerations • Data privacy: Strictly comply with GDPR and other relevant data protection regulations, and implement encryption, anonymization and other protection measures during data collection, storage, processing and transmission. • Anti-discrimination and fairness: Try to avoid stereotyping groups, be targeted and ensure that they do not deviate from the principle of fairness due to gender. • User knowledge and choice: Ensure that users understand the purpose of their data and provide corresponding privacy settings and exit options. In summary, by collecting legal data, building user profiles, and using algorithms such as clustering, collaborative filtering and supervised learning, shopping preferences can be mined from female users, thereby achieving more accurate advertising. However, transparency, respect for user privacy, and compliance with laws, regulations and ethical standards must be reflected throughout the process.