The following are some technical ideas and methods, but you need to be reminded that when designing a recommendation system, you should consider user experience, business goals, and ethical and legal constraints at the same time, avoid excessive labeling or implicit discrimination against a certain group, and ensure the transparency and fairness of the system. The solutions discussed below are only technical implementation ideas. Please implement them carefully according to the actual situation (including compliance requirements and corporate ethics). [1. Data collection and user portrait construction] ① Collect user basic data, behavior data, purchase records, click data, and product browsing data. ② For male users, build a detailed portrait, including historical purchase behavior, browsing preferences, search keywords, and social data. ③ Build product portraits for luxury goods and high-end electronic products, and extract data such as category, brand, price, and characteristics. [2. Feature engineering and data preprocessing] ① Separate signals related to high-end products (such as purchase frequency, brand sensitivity, price preference, etc.). ② Perform cluster analysis on male users, such as segmenting based on consumption level, interest preferences, etc. ③ Gender identifiers can be added to feature variables, but be careful to keep the labels transparent throughout the system, and do not let users feel deliberately manipulated when displaying recommendations. [3. Model selection and algorithm design] ① Collaborative filtering (User-based or Item-based): Recommend luxury goods and high-end electronic products based on the behavior of similar users or products; ② Content recommendation: Match based on the interest tags and product characteristics of male users; ③ Hybrid recommendation model: Combine collaborative filtering and content recommendation, set a certain weight, so that the proportion of luxury goods and high-end electronic products is increased when recommending to male users. [4. Commercialization regulation and priority sorting strategy] ① After generating the candidate recommendation list, the product can be post-processed (reranking): - Improve the weight based on the matching degree between the gender label and the product category (luxury goods/high-end electronic products); - Combine the user's historical preferences, set a specific preference template for male users, so that some products that meet the conditions are ranked higher. ② You can use reinforcement learning or multi-armed bandit algorithms to dynamically adjust the recommendation strategy, and conduct multiple experiments on products that are more likely to bring high conversion rates (i.e., high-gross-profit products); This requires the design of an appropriate reward function to take into account the actual needs and experience of users while pursuing business goals. [5. A/B testing and feedback iteration] ① Before actual application, verify the effect of the adjusted recommendation strategy through A/B testing: - Compare the performance of different versions in indicators such as click-through rate, conversion rate, and average order value; - Pay attention to monitoring whether there are obvious user disgust or complaints during the test. ② Continuously optimize model parameters and sorting rules based on feedback to ensure that the recommendation effect achieves both business goals and does not cause user discomfort. [6. Ethics and compliance notes] ① The goal is to recommend products to specific user groups at the commercial level, but be careful not to let the recommendation system become an invisible manipulation tool that undermines the user's right to choose independently. ② It is necessary to ensure that data collection and use comply with privacy protection policies, and explain the recommendation logic to users to maintain transparency. ③ At the same time, pay attention to relevant regulatory policies to ensure that algorithm recommendations do not constitute gender discrimination or other forms of bias. In summary, building a recommendation system for male users who prefer luxury goods and high-end electronic products depends on accurate user portraits and product feature extraction on the one hand, and on the other hand, it is necessary to give certain product categories a certain weight increase in the recommendation ranking (such as the model post-processing stage), and at the same time, through repeated testing and optimization, to achieve a balance between business goals and user experience. In the implementation process, ethical, transparent and compliance requirements must be taken into account.