Introduction

This lesson outlines the steps you will take to build the configurations needed for your recommender.

• Go through the steps to:

  • Identify the right kind of data for your project
  • Assign recommender model types
  • Configure deployment details

• Learn how to view and analyze your recommender dashboards to alter variables and increase recommendation effectiveness.

Recommenders are one of the most commonly used types of prediction when adding intelligence to customer interactions. They can be used for recommending products, messages, design constructs, special offers, and more. A recommendation can be made at any point in a customer journey when there is a choice of what to show to a customer.

Challenges Associated with Recommenders

1. Real-time recommenders add significant value by providing instant, contextually relevant suggestions, but they are challenging to implement correctly.
2. Recommendations are often deployed at critical points in customer journeys, meaning there is no margin for error in terms of uptime and responsiveness.
3. Some recommendation approaches require the building and management of a large number of models.
4. Offering discounts and special offers as part of recommendations can be costly, necessitating careful budget management.
5. Focusing on a single recommendation option limits the exploration of possibilities, which is necessary to keep up with changing human contexts.

Addressing Challenges with ecosystem.Ai

1. Clarifying Real-Time Complexity

Leveraging a scalable, low-latency infrastructure with robust data integration and processing capabilities, the platform uses online and continuous machine learning to keep models updated in real-time.

2. Managing High Stakes in Key Customer Journeys

Ensuring high availability and fault tolerance through distributed systems and redundant resources, the platform optimizes system performance to handle high traffic and ensure rapid response times.

3. Enhancing Model Management

Implementing automated tools for model management, including training, deployment, and monitoring, the platform uses a centralized system to manage multiple models efficiently and ensure consistency.

4. Ensuring Budget Management for Offers

Developing cost-efficient recommendation strategies that maximize ROI, the platform implements dynamic budgeting tools to adjust offers based on real-time data and performance metrics.

5. Balancing Exploration vs. Exploitation

Using reinforcement learning and multi-armed bandit algorithms to balance exploration and exploitation, the platform fosters a culture of continuous innovation and experimentation to stay ahead of market changes.