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A Hybrid Recommender System for Steam Games

  • Jin Gong
  • Yizhou Ye
  • Kostas StefanidisEmail author
Conference paper
  • 9 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1197)

Abstract

A recommender system can be considered as an information filtering system that seeks to predict the preference a user would have for a data item. It is commonly utilized in digital stores to recommend products to their users according to the users’ previous purchases. This applies to Steam as well, a widely used digital distribution platform for games. The existing recommender system mainly suggests new games to a given user by calculating similarities between games they own and those that they do not. These similarities are based on predefined attributes (game genres). Additionally, the system is able to recommend games based on the game preferences of the user’s friends. In this work, we target at creating an enhanced recommender system for Steam. The goal is to design a hybrid approach for producing suggestions that will utilize data, such as playing time, game price and game release date, in addition to the genres and the preferences of friends.

Keywords

Steam User profile Recommendation system Collaborative filtering 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tampere UniversityTampereFinland

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