Implementation of agent-based games recommendation system on mobile platforms

Abstract

Because of Google Play and App Store, today numerous different games are offered to every smartphone user. This diversity in supply is undoubtedly a good thing, but it also virtually disables users to find games they would like to play. However, it was shown that users tend to spend more money on purchasing recommended games when these recommendations are done using personal recommenders. In this paper we present an agent-based recommender for mobile platforms in which recommendations are made taking into account user game experience. Inputs for our recommender, which are collected both inside and outside the games, are stored in a semantic database. Based on collected information, user and game profiles are made that are then used in our recommendation algorithm. The focus of the paper is on how to do the implementation of the proposed system in real-world environments and obtain all the necessary data and how to make recommendations based on generated user and game profiles.