Case Study: AI-powered recommendation algorithm
Does AI-powered recommendation algorithm boost engagement of your users?
Project: Developing a recommendation algorithm to increase engagement on a social network
In the world of social networking, user engagement is a key success factor. One of the most effective ways to increase engagement is through personalised content recommendations. By delivering content that matches users' interests and behaviours, social networks can keep users engaged, increase time spent on the platform, and drive growth and revenue. This case study explores the development and results of a recommendation algorithm designed to transform the user experience on a social network.
Challenge: Increase user engagement through personalised content
Our client, a social networking platform, was faced with the challenge of low user engagement. To address this challenge, the client needed a sophisticated recommendation algorithm capable of delivering highly personalised content to each user. The goal was to create an algorithm that could understand users' interests, behaviours and interactions in order to suggest content that would keep them engaged and active on the platform.
Solution: AI recommendation algorithm
To develop an effective recommendation algorithm, we worked closely with the client to understand their specific needs and challenges. Our approach focused on using advanced machine learning techniques and embedding models to create a robust and scalable recommendation system.
Key components of the recommendation algorithm:
User and content embeddings: We used embedding models to represent users and content in a high-dimensional vector space. By capturing the underlying relationships between users and content, the embeddings allowed us to effectively measure similarity and relevance.

Behavioural analysis: We analysed users' interactions, such as likes, comments and viewing history, to understand their interests.
Content analysis: We used natural language processing (NLP) techniques to analyse the content of posts, including text, images and videos. By extracting meaningful features from the content, we ensured that the algorithm could accurately match users with relevant posts.
Collaborative filtering: We implemented collaborative filtering techniques to identify patterns and similarities between users.
Success: Increased engagement and user satisfaction
The implementation of the recommendation algorithm led to significant improvements in user engagement and satisfaction. Key achivements included: Increased time spent on the platform, Higher interaction rates, Reduced churn, and increased engagement on platform overall.