Steam Video Game and Bundle Data

Open Dataset
Creators:
Kang, Wang-Cheng; McAuley, Julian; Pathak, Apurva; Gupta, Kshitiz
Publication Date:
2018
Data Category:
Dataset Description:
This datasets collect user interactions and metadata from the Steam platform, aimed at facilitating research in recommendation systems and user behavior analysis. They encompass a vast number of user reviews, capturing detailed feedback and engagement levels. Also, it provides insights into game bundles, detailing which games are frequently purchased together, aiding in the analysis of bundling strategies and their effectiveness. The The dataset encompasses user reviews and interactions from October 2010 to January 2018 and is approximately 1.4 GB in size and includes:
  • Reviews: 7,793,069
  • Users: 2,567,538
  • Items: 15,474
  • Bundles: 615
Structurally, the dataset comprises several key components:​

  • User Reviews: Each review entry includes the user ID, game ID, review text, and associated metadata such as timestamps and ratings.​

  • Game Metadata: Information about each game, including game ID, title, genre, developer, and pricing details.​

  • Bundle Details: Descriptions of game bundles, specifying the bundle ID, included games, and pricing information.

 
Publications Citing This Dataset:
Haojie Ye, Sanketh Vedula, Yuhan Chen, Yichen Yang, Alex Bronstein, Ronald Dreslinski, Trevor Mudge, and Nishil Talati. 2023. GRACE: A Scalable Graph-Based Approach to Accelerating Recommendation Model Inference. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3 (ASPLOS 2023). Association for Computing Machinery, New York, NY, USA, 282–301. https://doi.org/10.1145/3582016.3582029
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