Steam games recommendation system
In the era of information overload, users can obtain massive game information through multiple channels. If users have a clear purpose, search is a very convenient and quick way to retrieve information. However, with the popularity of smartphones and mobile networks, users get more and more fragmented game time, and "killing time" has become the core goal of many products such as Steam.
People are lazy, if the game recommendation system can filter out irrelevant content for users in a timely manner, realize the game distribution function of thousands of people, help users filter and recommend games of interest, and let the right information find the right person , then this recommendation capability will significantly improve the user experience.
The Steam platform is a game and software platform that Valve Corporation hired BitTorrent (BT download) developer Bram Cohen to develop and design himself. The Steam platform is one of the world's largest comprehensive digital distribution platforms. Players can buy, download, discuss, upload and share games and software on the platform. On October 23, 2022, according to Steam statistics, the number of online players on the Steam platform in China exceeded the 30 million mark on the evening of the 23rd, reaching 30,013,151, setting a new historical record.
This project implements game recommendation and distribution algorithms based on a dataset containing more than 8,000 popular games on the steam game platform and 20,000 user information, including user-based recommendation, content (game)-based recommendation, and description-based recommendation. At the same time, we use JavaScript, Css, Html, React framework and Flask lightweight framework to achieve front-end and back-end development of the project.
| Official Full Name | Student ID (MTech Applicable) | Work Items (Who Did What) | Email (Optional) |
|---|---|---|---|
| Zhang Xingyu | A0261781B | Designed overall system architecture, independent front-end development and back-end development, cloud server build and environment configuration. | [email protected] |
| Peng Shaoze | A0261840J | Design recommendation system architecture, train and optimize recommendation model, data processing and exploration. | [email protected] |
| Tian Yuyang | A0261848U | Database structure design and database development, participate in product concept proposal and functional design. | [email protected] |
| Xu Xindi | A0261833E | Construct this system's backend with python, flask and SQLite, design system functions. | [email protected] |
System Modelling Video: Click here
Use Case Demo Video: Click here
Our steam games data and item-user data are from: