E-commerce Game Recommendation System

E-commerce Game Recommendation SystemAI Model Development

A Content-Based Recommender Using NLP and Cosine Similarity
← Back to Case Studies

Project Overview

This client, an online game retailer, needed to increase user engagement and average order value (AOV). Their store lacked a "smart" recommendations feature, causing customers to leave without discovering new products. We built a custom Content-Based AI Recommendation System from scratch.

😟 The Client's Problem: Low Product Discovery

The client's e-commerce store was functional but not "intelligent." Customers could search for games they already knew, but they weren't being introduced to new, relevant titles. This led to three main issues:

  • Generic User Experience: Every customer saw the same popular items, regardless of their interests.
  • Low Engagement: Users would view one product and then leave the site (high bounce rate).
  • Lost Cross-Sell Opportunities: The client was missing out on the "you might also like" feature, a proven method for increasing cart size.
  • "Cold Start" Problem: They didn't have enough user purchase data to build a traditional (Collaborative Filtering) recommender.

💡 The Solution: A Custom Content-Based Recommender

We built a Content-Based Recommendation System. This type of AI model is perfect for solving the "cold start" problem because it doesn't need user data. Instead, it analyzes the products themselves to find similarities.

  • Analyzes Game "Content": The model processes text-based features of each game (like its genre, description, tags, and developer) to understand its "DNA."
  • Calculates Similarity: Using Cosine Similarity (a core machine learning technique), the model builds a matrix that scores how "similar" each game is to every other game in the catalog.
  • Generates Real-Time Recommendations: When a user views a game (e.g., "Yakuza: Like A Dragon PC"), our recommendations function instantly finds and ranks the top 10 most similar games (like "Dirt 4 PC" or "Dishonored 2 PC") to show the user.
  • Delivered as a V 1.0 Model: We delivered the complete, annotated Jupyter Notebook to the client. This "base version" is a powerful, functional asset they can immediately use for testing, with clear pathways for future production-level improvements.

🛠️ Technology Stack & Key Services

This project showcases our expertise in building custom AI models from the ground up.

Key Services

  • AI Recommendation System Development
  • Content-Based Filtering Models
  • Machine Learning for E-commerce
  • Python Data Science
  • Jupyter Notebook AI Model Delivery
  • Cosine Similarity Recommender Engines
  • Natural Language Processing (NLP) for E-commerce

Technology Stack

  • Core Language: Python
  • Data Manipulation: Pandas, Numpy
  • Machine Learning: Scikit-learn (for TF-IDF/CountVectorizing text and calculating Cosine Similarity)
  • Data Visualization: Matplotlib, Seaborn (Used for initial data analysis)
  • Development Environment: Jupyter Notebook

⚙️ The Process: From Data to Deployed Model

  • Discovery & Data Analysis: We started with the client's product dataset. Using pandas, we analyzed the available features to identify the strongest "content" signals (e.g., genre, tags).
  • Feature Engineering (NLP): We processed and cleaned the raw text data. We then used a TF-IDF Vectorizer (a key NLP technique) to convert text-based tags and descriptions into a numerical format that the AI model can understand.
  • Model Building (Cosine Similarity): We built the core similarity matrix. This matrix is the "brain" of the recommender, containing the similarity score between every possible pair of games.
  • Function & Handover: We created the final recommendations function to make the model easy to use. We delivered the complete solution to the client, empowering their in-house team to integrate the logic into their e-commerce store's backend.
  • Future-Proofing: We provided a clear roadmap for "Version 2.0" improvements, such as evolving this into a Hybrid Model (combining content-based and user-based data) as they collect more sales data.

🏆 The Result: A Functional AI Asset and a Path to Growth

This "base version" model was a significant win for the client, delivering immediate and long-term value.

  • A Tangible AI Asset: The client received a custom-built, functional AI model, not just a theoretical report. They own the code and can build upon it.
  • Solved the "Cold Start" Problem: The client can now provide relevant recommendations to all users, including brand-new visitors.
  • Clear Path to Production: The V1.0 model proved the concept and provided the foundation for a full-scale production system, dramatically reducing future development risk.
  • Empowered In-House Team: The client's developers could immediately take our notebook, integrate the function, and begin A/B testing a "You Might Also Like" feature on their live store.