Steam-200k Recommender System (Implicit ALS)

Overview

This project builds a Top-N game recommendation tool using the Steam-200k dataset. It models user-game interactions and recommends games based on similarity between player profiles.

What I built

• Implemented a recommender pipeline using implicit-feedback ALS (Alternating Least Squares).
• Transformed gameplay behavior into user preference signals to create user profiles.
• Generated Top-N recommendations by identifying players with similar profiles and surfacing games they play that the user hasn’t seen.

Approach

• Treat play behavior as implicit feedback rather than explicit ratings.
• Learn latent factors for users and games using ALS.
• Recommend games with the highest predicted relevance for each user.

Tools & tech

Python, implicit ALS, matrix factorization, data preprocessing, evaluation/validation

Outcome

A working recommendation tool that produces personalized Top-N suggestions from large-scale interaction data.