Development of an algorithm for improving relevance of recommendations in online dating services using neural networks

The presented article examines an innovative algorithm for assessing the attractiveness of potential partners in the context of online dating. The algorithm employs two neural networks: a generative network and a convolutional network. The generative neural network creates visual profiles based on various attractiveness parameters, while the convolutional neural network analyzes and extracts these parameters from images of real users. This approach allows for the dynamic adaptation of user preferences, ensuring high relevance of recommendations even with a limited pool of candidates in a given region. The method described in the article aims to significantly enhance the user experience and increase the success rate of online dating. By utilizing neural networks, the algorithm can account for individual user preferences and adapt to them in real-time. This makes the recommendations more accurate and personalized, which in turn facilitates the creation of deeper and higher-quality interpersonal connections. The research also emphasizes the importance of forming stable and happy long-term relationships. The presented approach contributes to this by providing users with a more satisfactory and effective experience in online dating. Thus, the use of algorithms and neural networks in the field of online dating has the potential to greatly improve the quality of interactions and interpersonal connections, which is a crucial aspect in the modern digital age.

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