Meep.ai 1.1.4

1. Meet in Map: We utilize advanced algorithms to filter nearby users and display them on the map. Our recommendation algorithm leverages deep learning to extract personalized features and latent variables, effectively analyzing a range of information including geographic location, user interests, occupational fields, and group memberships. Moreover, it incorporates large language models to deduce language styles and topics of interest from personal chat data. Ultimately, a variety of distance functions are employed to rank high-dimensional feature latent variables for optimal recommendations.

2. Meet in Group: Our emphasis lies in fostering online growth through a methodical "Group by Group" and "Club by Club" approach. This strategy aims to create densely populated user clusters within specific areas while upholding mutual trust and basic understanding among users. This unique model grants club members and esteemed community participants the chance to convene offline and engage in invaluable face-to-face interactions.

3. Meet up: Our core objective revolves around facilitating authentic face-to-face meetups among users. Through our meticulously crafted algorithms and operational modes, we ensure the credibility and immediacy of these gatherings. A key performance indicator (KPI) that we notably prioritize in our product is the average number of meetups per user per week.