A SteamAPI-powered game recommendation web app that learns user taste from liked games and returns high-similarity suggestions with game profile data. NextQuest uses a multimodal retrieval pipeline (text + image + numeric features) with FAISS indexing, delivered through FastAPI and a React frontend.

Overview

• SteamID-based flow with two game selection modes:
  • Steam Library
  • All Catalog Games
• Recommender excludes games already owned in the user’s library.
• Selection and recommendation cards support in-app profile modal previews.
• Profile modal includes metadata, score badges, description, and screenshots.
• Stack:
  • React (frontend)
  • FastAPI (backend)
  • FAISS + Sentence Transformers (retrieval/index)

Steam Data

NextQuest uses Steam APIs for:

• owned library retrieval (profile dependent)
• app details metadata
• review snapshots
• current player counts Notes:
• Your steam ID can be found under account details on the Steam website. It's a 17-digit ID.
• some profiles may be private or unavailable
• metadata completeness can vary by title

Note: This repository does not include runtime data. Download the full data/ folder from the Hugging Face repo and place it at the project root (same level as frontend/, scripts/, and README.md) before training or running inference locally.

Training

If you want to rebuild the catalog and recommender artifacts locally:

conda env create -f environment.yml
conda activate nextquest

# Data collection and processing
python -m scripts.pipeline.collect_steam_game_data --seed-games-path data/seed_games_200.csv --raw-data-directory archive/archived_data/raw/steam --processed-data-directory data/processed --reviews-per-game 50

# Feature engineering and artifact building
python -m scripts.pipeline.build_recommender_features --games-path data/processed/games_catalog.parquet --reviews-path data/processed/reviews_catalog.parquet --features-path data/features/game_features.parquet

# Index building
python -m scripts.pipeline.build_game_genome_index --features-path data/features/game_features.parquet --artifacts-directory data/artifacts --images-per-game 3 --similar-games-count 30

Local Usage

To run the app locally after building artifacts, you must run the backend and frontend separately:

# In one terminal, start the backend
uvicorn scripts.api.main:app --reload --port 8000

# In another terminal, start the frontend
cd frontend
npm install
node ./node_modules/vite/bin/vite.js

Open:

• Frontend: http://localhost:5173
• Backend: http://localhost:8000

Live App: https://nextquest-ochre.vercel.app/

Citations

FAISS

@article{johnson2019billion,
  title={Billion-scale similarity search with GPUs},
  author={Johnson, Jeff and Douze, Matthijs and J{\'e}gou, Herv{\'e}},
  journal={IEEE Transactions on Big Data},
  year={2019}
}

Sentence-BERT / Sentence Transformers

@inproceedings{reimers2019sentence,
  title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
  author={Reimers, Nils and Gurevych, Iryna},
  booktitle={EMNLP-IJCNLP},
  year={2019}
}

CLIP

@inproceedings{radford2021learning,
  title={Learning Transferable Visual Models From Natural Language Supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and et al.},
  booktitle={ICML},
  year={2021}
}

License

This project is released under the MIT License.