# Collect female user shopping preferences through algorithms to optimize advertising delivery To design a system that respects user privacy while improving advertising accuracy, you can consider the following methods： ## Data collection method -**Behavioral data expressly authorized by the user**: browsing history, residence time, click path -**Transaction history analysis**: purchase frequency, price sensitivity, category preference -**Preferences voluntarily provided by users**: questionnaire survey, preference label selection -**A/B test feedback**: Comparison of response rates of different styles of advertising ## Algorithm strategy 1. **Collaborative filtering algorithm**: Based on the principle of "similar users like similar products" 2. **Content analysis algorithm**: Establish a preference model based on product characteristics and user historical interaction 3. **Sequence pattern mining**: Identify time series patterns of user shopping behavior 4. **Seasonal and cyclical analysis**: Capture the time pattern of female users shopping ## Privacy protection measures -Implement data anonymity processing -Provide a transparent privacy policy -Allows users to adjust or revoke data usage authorization at any time -Comply with relevant data protection regulations This method can not only improve the accuracy of advertising, but also establish a sustainable personalized recommendation system while respecting user privacy.