Work

Asmodee Recommendation System

Case Study 14 mars 2025
Graph Theory
Recommender System
Clustering
Data Science
Product Strategy

We modeled player behaviors and game relationships to deliver a personalized gaming experience on Board Game Arena.

Network graph and clustering interface from the Asmodee project

Description

As part of Asmodee’s Business Deep Dive, we worked on a massive dataset of over 200 million rows from Board Game Arena to analyze player behavior and game interactions. With Anna Spira, we combined graph modeling, clustering, and recommendation techniques to design a personalized, data-driven gaming experience.

Key Features

  • Game Graph Visualization: We built an interactive graph of 1,100+ games, where edges were classified using cosine similarity as complementary or competitive. The graph was enriched via the Board Game Geek API and allowed users to dynamically add new games.

  • Player Segmentation: Using hierarchical clustering and Gaussian Mixture Models, we segmented players into six profiles based on activity, engagement, and reputation metrics.

  • Recommender Engine: We developed a custom algorithm combining graph proximity, user segment, and business rules to suggest relevant games for each type of player.

Technologies Used

  • Python: Core language for all data processing and pipeline logic.
  • Pandas: Data wrangling on 200M+ rows of gameplay data.
  • NumPy: Numerical operations and cosine similarity computations.
  • NetworkX: Graph creation and game relationship modeling.
  • Scikit-learn: GMM, hierarchical clustering, and preprocessing.
  • Plotly: Interactive visualizations for the recommendation interface.

Roles and Contributions

I developed the full game graph pipeline, from API integration and similarity computation to front-end interaction. I also participated in the segmentation pipeline and helped design the recommender logic.

Outcome

Our work was selected as a finalist and highlighted for its ability to transform complex behavioral data into a clear, actionable product strategy. The interactive graph and recommender system gave Asmodee tools to better engage and retain their player base.