Metadata-Version: 2.1
Name: board-game-recommender
Version: 2.0.0
Summary: Board games recommender engine
Home-page: https://recommend.games/
Author: Markus Shepherd
Author-email: recommend.ludoj@gmail.com
License: MIT
Project-URL: Documentation, https://gitlab.com/recommend.games/board-game-recommender/blob/master/README.md
Project-URL: Source, https://gitlab.com/recommend.games/board-game-recommender
Project-URL: Tracker, https://gitlab.com/recommend.games/board-game-recommender/issues
Description: 
        # board-game-recommender
        Board game recommendation engine. View the recommendations live at [Recommend.Games](https://recommend.games/)! Install via
        ```bash
        pip install board-game-recommender
        ```
        
        ## Training new recommender models
        
        ### Environment
        [Requires Python 3](https://pythonclock.org/). Make sure [Pipenv](https://docs.pipenv.org/) is installed and create the virtual environment:
        ```bash
        python3 -m pip install --upgrade pipenv
        pipenv install --dev
        pipenv shell
        ```
        
        ### Datasets
        In order to train the models you will need appropriate game and rating data. You can either scrape your own using the [board-game-scraper](https://gitlab.com/recommend.games/board-game-scraper) project or take a look at the [BoardGameGeek guild](https://boardgamegeek.com/thread/2287371/boardgamegeek-games-and-ratings-datasets) to obtain existing datasets.
        
        At the moment there are [recommender implementations](board_game_recommender/recommend.py) for two sources: [BoardGameGeek](https://boardgamegeek.com/) and [Board Game Atlas](https://www.boardgameatlas.com/).
        
        ### Models
        We use the recommender implementation by [Turi Create](https://github.com/apple/turicreate). Two recommender models are supported out of the box:
        * [`RankingFactorizationRecommender`](https://apple.github.io/turicreate/docs/api/generated/turicreate.recommender.ranking_factorization_recommender.RankingFactorizationRecommender.html) (default): Learns latent factors for each user and game, generally yielding very interesting recommendations.
        * [`ItemSimilarityRecommender`](https://apple.github.io/turicreate/docs/api/generated/turicreate.recommender.item_similarity_recommender.ItemSimilarityRecommender.html): Ranks a game according to its similarity to other ratings by a user, often resulting in less interesting recommendations. However, this model is also able to find games similar to a given game.
        
        ### Run the training
        Run the training via the [main script](board_game_recommender/__main__.py):
        ```bash
        python -m board_game_recommender --help
        ```
        E.g., train the default BGG mode like so:
        ```bash
        python -m board_game_recommender \
            --train \
            --games-file bgg_GameItem.jl \
            --ratings-file bgg_RatingItem.jl \
            --model model/output/dir
        ```
        
        ## Links
        * [board-game-recommender](https://gitlab.com/recommend.games/board-game-recommender): This repository
        * [Recommend.Games](https://recommend.games/): board game recommender website
        * [recommend-games-server](https://gitlab.com/recommend.games/recommend-games-server): Server code for [Recommend.Games](https://recommend.games/)
        * [board-game-scraper](https://gitlab.com/recommend.games/board-game-scraper): Board game data scraper
        
Keywords: board games,tabletop games,recommendations,recommender,recommender engine,recommendation engine,boardgamegeek,bgg,ratings,turicreate,collaborative filtering,ludoj,ludoj-recommender
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Games/Entertainment :: Board Games
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6.0,<3.7.0
Description-Content-Type: text/markdown
