The following is a technical implementation idea, but it is important to remind you that when designing a dating app, deliberately providing priority matching suggestions for a certain gender may cause ethical disputes and legal risks. In actual products, please be sure to comply with local anti-discrimination laws and industry regulations, and fully consider user experience and fairness. [Overview of ideas] 1. User attribute and preference modeling • Collect information such as gender, interests, geographic location, age, preferences, etc. when users register. • Establish user tags (Tags) and interest vectors as basic factors for subsequent matching. 2. Matching scoring system • Define a basic matching scoring function: calculate a basic matching score based on the common interests, preferences, geographic location distance, etc. between users. • Construct a "reverse candidate pool" for each user, that is, preliminarily screen out a set of users who may match based on the preferences of both parties. 3. Priority weighting strategy (to achieve priority recommendation of a certain gender) • In the candidate pool, after calculating the basic matching score based on the other party's attributes, introduce a weighting factor. • If the candidate user belongs to the "priority matching gender", multiply its basic score by a weight coefficient greater than 1, such as 1.2 or 1.3; if the candidate user does not belong to this gender, keep the original score. • Formula example: Final match score = Basic match score × Weight factor Where weight factor = 1.0 (non-preferred gender) or 1.2 (preferred gender). 4. Sorting and recommendation logic • Sort the candidate user list according to the final match score; • When showing recommendations to users, it can ensure that users of the preferred gender have a higher ranking and appear in the recommendation list earlier. 5. Dynamic adjustment and feedback • Collect user feedback (such as whether they are interested in matching suggestions, behavioral data, etc.) and dynamically adjust the weight factor; • Regularly monitor matching effects and user experience to ensure that the system is balanced and does not cause the larger party to be unable to obtain reasonable matches. [Technical Implementation Notes] • Parameter adjustment: In the early stage of design, A/B testing can be used to verify the impact of different weights on matching effects, user stickiness, etc., and then optimize the settings after collecting data. • Transparency and compliance review: In the process of product design and operation, privacy, transparency and fairness issues that may cause users to question should be clearly known, and legal and ethical experts should be communicated when necessary. • User personalized needs: In addition to gender priority, other dimensions of personalized matching can also be considered, such as region, interests, and behavioral habits, so as to balance system fairness while maintaining business goals. [Summary] The core of this matching algorithm is to introduce a gender-related weighting factor in the matching scoring process, so that users of the target gender can gain a certain advantage in the ranking. During implementation, the social impact and legal risks of this strategy must be carefully evaluated to ensure that while achieving business goals, it does not harm user rights or violate relevant laws and regulations.