Overcoming the Cold-Start Problem: How AI and Chatbots Revolutionize User Experience

Introduction

Five years ago, I left my data scientist job at a bank to create an AI-driven job search engine called HiredHippo. Despite its potential, the startup eventually failed, leaving me with many questions about what went wrong. After much reflection, I realized the most significant issue we faced was the dreaded cold-start problem. In this article, I will discuss the cold-start problem, our attempts to solve it, and how recent advances in AI and chatbots are revolutionizing user experience and offering new solutions.

The Cold-Start Problem in HiredHippo

The cold-start problem refers to the challenge of attracting and retaining users when there are few or no other users on a platform. This lack of users creates a vicious cycle where potential users are less likely to join and participate, further reducing the value of the platform. For HiredHippo, we struggled to retain users beyond their first few sessions because our recommendations simply weren't useful enough, failing to outperform alternatives like Indeed and LinkedIn.

Our Attempts to Solve the Cold-Start Problem

We focused heavily on the first users by parsing their resumes to fill their profiles, fine-tuning an onboarding questionnaire, and continuously tuning our algorithms based on user feedback. However, these efforts still fell short of user expectations. We were playing the quality game, not the volume game like our competitors, and if we couldn't beat them clearly on quality, we were doomed.

The Game Changer: ChatGPT and Large Language Models

Today, AI advancements like ChatGPT have revolutionized user experience. Rigid questionnaires with pre-selected questions and answer options have been replaced with smart prompts, contexts, and free-flowing dialogue. Large Language Models powering such chatbots enable a comprehensive understanding of large volumes of text, making it possible to identify similar documents of very different origins or even mediums.

Introducing Fastglide: A New Solution to the Cold-Start Problem

We are now launching Fastglide, a new product that combines the power of chatbots with a state-of-the-art recommendation system to help tackle cold-start problems. Fastglide engages newly landed users in free-flowing conversation, continuously gathers their preferences and profile information, provides real-time content recommendations, and primes your applications for success. With Fastglide, we aim to transform the user experience and overcome the cold-start problem that plagued our previous venture.

Conversational AI Front-End

Fastglide utilizes a conversational AI front-end to engage users in a natural and interactive conversation. As users chat with the AI, the system continuously learns about their preferences, interests, and needs. This dynamic process makes for a more natural and engaging user experience over existing UX paradigms.

Real-Time Content Recommendations

Simultaneously, Fastglide's recommendation system pulls relevant content based on the ongoing conversation, displaying it alongside the chat messages. As the conversation progresses and new messages are received, the system refreshes the recommended content, ensuring that users are always presented with the most suitable options.

Seamless Integration with Main Product Backend

While users interact with Fastglide's conversational AI, the system aggregates user engagement information and combines it with profile data extracted from the conversation. This enriched data is then sent to the main product backend, creating a comprehensive user profile that enhances the overall product experience.

Smooth Transition to Main Product Pages

Once enough information is gathered, users can effortlessly transition from the conversation screen to the main product pages. With Fastglide, users receive a tailored experience from the start, making it easier for them to find value in the platform and increasing the likelihood of long-term retention.

By integrating these key components, Fastglide offers a unique solution to the cold-start problem, delivering an engaging user experience while simultaneously gathering valuable data for personalized recommendations and seamless integration with the main product.

High-Level Design of Fastglide: Microservices Architecture

Fastglide's high-level design consists of four key components:

1. Conversational AI Microservice

2. Recommendation System (Recsys) Microservice

3. Analysis Microservice

4. Message Queue Service

These components, along with an internal database, are hosted on a dedicated Kubernetes cluster provided by a cloud service provider such as AWS, Azure, or GCP. This architecture allows for seamless integration and ensures the scalability and reliability of Fastglide's infrastructure.

Conversational AI Microservice

The Conversational AI Microservice generates message responses to users based on their input. This microservice pulls relevant history data and outputs of analysis calls from the internal database to generate messages and provide the status of the conversation, such as whether the user profile is complete enough.

Recommendation System (Recsys) Microservice

The Recsys Microservice provides content recommendations by checking the internal database for relevant messages, the content catalog, and user interactions with the content. The product front-end queries this microservice to display personalized content recommendations to the user.

Analysis Microservice

The Analysis Microservice is triggered as a background process to analyze user profiles and update the status of the conversation in the internal database. This microservice also picks up user interaction events from the Message Queue Service and updates the database accordingly.

Message Queue Service

The Message Queue Service, hosted by the cloud service provider (e.g., Cloud Pub/Sub on GCP or Messaging Service on Azure), receives user interaction events captured by the product front-end. These events are then processed by the Analysis Microservice, enabling continuous improvement of content recommendations.

Main Product Backend

The main product backend queries the internal database for user profile information and any other relevant data, using this information to enhance the overall user experience on the platform.