Revolutionizing Real-Time Machine Learning for Social Networks
In a Startup Scout podcast episode, Ben, the co-founder of Superlinked, shared his insights on how the company is helping democratize access to real-time Machine Learning (ML) infrastructure. As a seasoned entrepreneur, product manager, and strategist, Ben has experience in building consumer products at Orange during the "golden age" of telecoms, helping Fortune 500 corporations reignite growth at McKinsey, and conducting commercial due diligence at PwC.
User Modeling for Enhanced Personalization
At the core of Superlinked's approach is user modeling, a process that helps in personalizing user experiences. This is an important aspect in creating relevant content for users. For instance, a job board may want to show jobs that match the seniority level of the user, ensuring that directors or senior-level professionals do not see internships or entry-level positions. User modeling goes beyond seniority and recency factors to create a comprehensive, personalized experience for users. By leveraging ML algorithms and the vast amount of data obtained from user interactions, Superlinked can understand user preferences, interests, and behaviors on a deeper level. This enables the system to provide highly relevant content, connections, and recommendations that align with each user's unique needs and preferences.
Pre-trained Models for Personalized Experiences
One of the primary concerns raised by smaller social networks is the lack of sufficient data to develop effective ML algorithms. Superlinked tackles this problem by offering pre-trained models, specifically tailored for professional contexts. These models have been trained on tens of thousands of profiles and are designed to work out of the box, eliminating the need for an extensive dataset.
Furthermore, Superlinked works with customers to understand their needs and deliver personalized experiences. The company's configuration file serves as a bridge between the pre-trained models and the client's unique requirements. Superlinked currently supports clients in configuring the models to ensure they deliver on the Key Performance Indicators (KPIs) that matter most to the client's business.
Competing Solutions and Their Challenges
While there are some established solutions in the e-commerce space, such as Algolia Recommend and AWS Personalize, Ben notes that they face two significant challenges:
1. The Black Box Problem:
With these solutions, clients often have limited control over the recommendations generated. They cannot easily specify what factors are more important to them, leading to a lack of customization. Superlinked addresses this issue through its configuration file, allowing clients to customize the model for their specific use case and product.
Unlike Superlinked, both Algolia Recommend and AWS Personalize work on batch processing, with AWS Personalize having a minimum training frame of two hours. In a social media context, waiting for hours to receive relevant content recommendations is not feasible. Users expect instant, tailored experiences, and a failure to meet these expectations can result in high churn rates.
Superlinked's Solution: Real-Time and Customizable
By providing real-time recommendations and a high degree of customizability, Superlinked is revolutionizing the way social networks approach personalization. With its unique configuration capabilities and focus on user modeling, Superlinked empowers clients to create engaging, relevant experiences that drive user retention and satisfaction.
To get more insights, watch the full podact: