Steam Simularity Reccomender Student Project [p]

Short summary

A student developer built a Steam game recommender that surfaces games based on specific gameplay and aesthetic preferences—like 'card synergies' or 'jazz fusion vibes'—extracted via LLM analysis of 2k reviews per game and stored in vector embeddings. The system explains its recommendations using micro-tags and is live at nextsteamgame.com with open-source code on GitHub. It demonstrates a practical pattern for explainable, vector-based recommendations that avoids collaborative filtering's 'echo chamber' problem.

• •Built Steam recommender using vector embeddings and LLM-extracted micro-tags from 2k reviews per game
• •Explains recommendations by showing specific game aspects (card synergies, vibes, gameplay focus)
• •Open-source project deployed live—shows pattern for explainable discovery vs. collaborative filtering

Generated with AI, which can make mistakes.