This project was created as the capstone project for BrainStation's Data Science Diploma program. The purpose of this project is to create a recommender algorithm for the Steam game store. The end goal is an algorithm that can read a users play history and identify titles it believes the user would enjoy. Currently it implements a FunkSVDpp algorithm using two different rating metrics. In the near future hybrid models are intended to be implemented to allow greater variety and customizability of recommendations.
The version of the dataset that was used for this project can be downloaded here.
These datasets were sourced from Kaggle, the original poster specified a monthly update frequency so you can potentially source an updated dataset here.
The file consists of three separates csv's and a JSON file.
games.csv features a breakdown of all the games available for sale in the Steam store. The intial csv contains basic data that could create a base recommender system. The JSON file included however contains additional, non tabular, information on games such as descriptions and tags. For our purposes we will have to extract and connect at least some of that information to this file.
Column breakdown of games.csv is as follows:
- app_id: Unique id for each individual game in the games.csv
- title: Title of game
- date_release: Release date of the game
- win: Game is playable on Windows
- mac: Game is playable on Mac
- linux: Game is playable on Linux
- rating: Overall user rating of game
- positive_ratio: The ratio of positive user reviews to negative user reviews by game
- user_reviews: Number of unique user reviews on a game
- price_final: Current price of game as of last data scrape
- price_original: Original price of game on release (specifically on Steam store release)
- discount: Whether the game is discounted as of last data scrape (and by how much in %)
- steam_deck: Whether game is playable on Steam's handheld device, the Steam Deck
| app_id | title | date_release | win | mac | linux | rating | positive_ratio | user_reviews | price_final | price_original | discount | steam_deck |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13500 | Prince of Persia: Warrior Within™ | 2008-11-21 | true | false | false | Very Positive | 84 | 2199 | 9.99 | 9.99 | 0.0 | true |
users.csv contains information on unique Steam users. Though the information is sparse it could potentially be used to 'grade' recommendations based on whether the user is a notable one.
Column breakdown of users.csv is as follows:
- user_id: Unique id for each individual user of the Steam store. Useful for connecting back to recommendations.csv
- products: Number of products owned by unique user
- reviews: Number of reviews posted by unique user
| user_id | products | reviews |
|---|---|---|
| 7360263 | 359 | 0 |
recommendations.csv is the largest file of the three and focuses on individual user recommendations for video games. This will be the primary column in determinine whether a game is performing well and whether a review could be a good or bad indicator of game success.
Column breakdown of recommendations.csv is as follows:
- app_id: Unique id of the game being reviewed, can be tied back to app_id in games.csv
- helpful: How many users voted this review as 'helpful'
- funny: How many users voted this review as 'funny'
- date: Date of review
- is_recommended: Whether the user recommends the game or not
- hours: Hours of playtime the user has on the game
- user_id: Unique user id that can be tied back to users.csv
- review_id: Auto-generated label for review number by row. Could potentially be deleted.
| app_id | helpful | funny | date | is_recommended | hours | user_id | review_id |
|---|---|---|---|---|---|---|---|
| 975370 | 0 | 0 | 2022-12-12 | True | 36.3 | 51580 | 0 |
- Initial GitHub repository and project breakdown
- Preliminary EDA on the data, including cleaning and noting any problem areas
- Extraction of JSON file and merging pertinent data into existing csv, cleaning data again once completed
- Discover usable metrics which might lead to an initial model
- Begin prototyping model
- Build and test first model
- Create a recommendation function
- Implement hybrid model features to refine recommendation output
- Package everything into a Streamlit app