In the process of improving the accuracy of advertising, collecting and analyzing user data is the key, but it is necessary to strictly comply with privacy protection laws and regulations (such as GDPR, CCPA, China's "Personal Information Protection Law", etc.), and ensure that the processing of sensitive information such as gender complies with ethical requirements. The following is the step-by-step technical implementation framework and precautions： --- ###**1. Collection and classification of compliance data** 1. **Dominant preference data** -**Behavioral data**: Collect the user's browsing path in the application (page stay time, product click order), search keywords (such as “summer dress”), additional purchase/collection behavior, purchase records, return records, etc. through buried points. -**Feedback data**: Users' click-through rate (CTR), conversion rate (CVR) of advertisements, keywords in ratings/comments (such as extracting preference tags such as “comfortable” and “thin” through NLP). 2. **Inference of hidden preferences** -Cross-platform behavior correlation**: If it is compliant and authorized by the user, it can integrate the public behavior data of users on social media (such as likes for clothing content) and third-party e-commerce platforms (through SDKs or APIs) to build a cross-domain interest graph. -**Timing pattern analysis**: Identify shopping cycles (such as monthly purchases of beauty products), seasonal preferences (such as winter preferences for down jackets), and promotional sensitivity (such as the response rate to discount coupons). 3. **Demographic and contextual information** -**Legally obtained tags**: After the user expressly agrees, the age group, geographic location, and family role (such as maternal and child user groups) are obtained through registration information or questionnaires. -**Environmental data**: Optimize real-time recommendations based on usage scenarios (such as browsing professional wear at noon on weekdays) and equipment types (mobile terminals are more inclined to FMCG products). --- ###**2. Algorithm model construction** 1. **Feature engineering** -Construct multi-dimensional feature vectors, such as： -Short-term interest: Capture the dynamic preference of the current browsing sequence through a session-based model (such as GRU). -Long-term interest: Using Matrix Factorization to extract hidden semantic features (such as “Korean style” and “organic composition”) from historical behaviors. -Social network characteristics: Based on graph neural network (GNN), analyze the spread of interest in users' social relationships (such as similar shopping preferences in the circle of girlfriends). 2. **Model selection and training** -**Collaborative filtering upgrade**: Use deep learning enhanced collaborative filtering (such as NeuMF) to solve sparsity problems, and predict unobserved preferences through user-commodity interaction data. -**Multitasking learning**: Jointly optimize the prediction of click-through rate (CTR) and conversion rate (CVR) to avoid single indicator deviations (for example, only optimizing CTR may lead to high clicks and low purchases). -**Real-time update mechanism**: The streaming computing framework (such as Apache Flink) is used to realize online model learning and quickly respond to changes in user behavior. 3. **Interpretability and fairness** -Introduce SHAP values (SHAPLEY Additive exPlanations) to explain the recommended results and avoid gender stereotypes (for example, it should not be assumed that female users only focus on maternal and child or beauty makeup). -Regularly detect model deviations through A/B testing, such as comparing the fairness of advertising exposure for users of different genders (through fairness indicators such as Demographic Parity). --- ###**3. Privacy protection and compliance measures** 1. **Data minimization principle** -Only collect the necessary data directly related to advertising recommendations to avoid excessive collection (such as not forcibly obtaining the user's address book). -Anonymous processing: Use Differential Privacy to add noise to aggregated statistics, or implement localized model training through Federated Learning. 2. **User control** -Provide a preference management panel: allows users to manually adjust interest tags (such as turning off “beauty recommendations” and adding “sports equipment” preferences). -Opt-out mechanism: The “Contextual Advertising” option is explicitly provided, relying only on page content and not personal data to serve advertisements. 3. **Secure storage and transmission** -Encrypt sensitive data (such as using AES-256) and restrict internal data access through role permission control (RBAC). -Conduct regular security audits and vulnerability scans to prevent the risk of data leakage. --- ###**Fourth, effect evaluation and iteration** 1. **Monitoring of core indicators** -Accuracy: Evaluate the correlation between the recommended list and the user's true preferences through NDCG (Normalized Discounted Cumulative Gain). -Business value: track the proportion of increase in ADVERTISING ROI (Return on Investment) and GMV (Gross Merchandise Volume). -User experience: monitor the user complaint rate, the frequency of use of the preference panel, etc. 2. **Continuous optimization strategy** -**Cold start problem**: For new users, Content-based filtering or Lookalike modeling are used for similar user groups. -**Long-tail product coverage**: Balance the exposure opportunities of popular products and long-tail products through Reinforcement Learning. --- ###**Ethics and Risk Warning** -**Avoid gender bias**: It is forbidden to use gender as