Jun 24, 2022
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Recommender System for the Social Network

Recommender System for the Social Network

more 1 year
United States
Service categories
Service Lines
Artificial Intelligence
Mobile Development
Web Development
Domain focus
Media & Entertainment
Programming language


Geosocial networking apps are a relatively new types of social apps. The most popular players on the market are Yelp, Facebook Places, and Foursquare. These apps allow users to share their locations as well as find recommendations for locations or 'venues'. ► The client’s main idea was to change a commonly-used approach to social media posting and social networking apps, add voice and language recognition and create a unique recommendation system; ► Our client was looking for a development team with experience in Machine Learning; ► Our client wanted to start as fast as possible and create a working prototype of a recommendation model for his investors.


Our team was in charge of the project, starting with the discovery phase and POC, and finishing with the development of the app prototype. ► The app automatically recognizes the event, date, time, and location of the user thanks to Named Entity Recognition ("What", "Where", "When") or voice assistant. Based on this information, the user receives a list of recommendations. ► Social communication is possible through the internal chat platform with the functionality to post photos and video files, and comment posts of other users. ► To cope with a large number of user data, our ML experts have built a knowledge graph of the social network. This custom database helps save and structure different facts about clients for further analysis.


► Our Business Analysts conducted profound research of the geosocial apps market, defining competition and the most popular features. ► We developed a customizable recommendation system, based on users’ geolocation data in two languages: English and Vietnamese. ► We made our solution bilingual for English and Vietnamese-speaking users. ► We created a knowledge graph, with a lot of valuable user’s data for further analysis. ► We also added syntactic text simplification of complex English sentences in the graph for developers convenience.