AI-Powered Game Recommendation System Using NLP (SteamMatch)

SteamMatch is an intelligent game recommendation system designed to solve the cold-start and sparse data problem in gaming platforms.

The system leverages Natural Language Processing (NLP) techniques to analyze game descriptions and user preferences, combining TF-IDF and Word2Vec embeddings to build a hybrid recommendation engine that improves personalization accuracy.

Key Features:

Hybrid NLP-based recommendation engine (TF-IDF + Word2Vec)

84% Precision@10 performance

35% improvement in cold-start scenarios

Content-based filtering approach

Clean backend implementation using Flask

Implementation Process:

The project followed a structured data science workflow starting with text preprocessing, tokenization, vectorization, and feature engineering. Word embeddings were generated using Gensim, and similarity scoring was applied to rank recommendations.

The backend was implemented with Flask APIs to simulate real-world deployment scenarios, demonstrating the ability to transform NLP models into practical, scalable applications.

SteamMatch showcases strong expertise in NLP, recommendation systems, and applied machine learning.