To set up a personalized recommendation system for male users on shopping websites to increase their purchase tendency of luxury goods and high-end electronic products, the following strategies can be adopted: 1. **User portrait construction**: By collecting data such as users' historical browsing, purchasing behavior, and search history, a detailed user portrait is constructed to understand their preferences, consumption levels, and possible needs. 2. **Clustering strategy**: Based on the user portrait, male users are divided into different groups (such as young professionals, married men with families, etc.), and different recommendation strategies are formulated for different groups. 3. **Collaborative filtering and content recommendation**: - Use collaborative filtering algorithms to recommend products based on the behavior of similar users. - Use content recommendation algorithms to recommend similar luxury goods and high-end electronic products based on the characteristics of the products (brand, function, price range). 4. **Enhance recommendation accuracy**: Combine deep learning and machine learning algorithms to improve the accuracy of recommendations, such as using neural network models to learn users' complex behavior patterns. 5. **Scenario-based recommendation**: During specific shopping festivals or promotional events, luxury goods and high-end electronic products are recommended, such as Valentine's Day, Double Eleven, e-commerce promotions, etc. 6. **Strengthen branding and value creation**: Add brand stories, user reviews, usage scenarios and other content related to luxury goods and high-end electronic products to the recommendation page to enhance product appeal. 7. **Personalized marketing**: Send personalized discount information, gift certificates and limited-time offers through email, SMS or App push to stimulate purchasing interest. 8. **User feedback mechanism**: Establish a user feedback system to regularly collect user reviews and suggestions on recommended products and continuously optimize the recommendation algorithm. 9. **A/B testing**: Conduct A/B testing on different recommendation strategies to observe which strategies have a more positive impact on male users' purchasing decisions and continuously improve the recommendation system. The above strategies will help increase male users' willingness to purchase luxury goods and high-end electronic products and improve the overall user experience.