Attainment Ratings for Graph-Query Recommendation

TL;DR

This paper presents a graph-based recommender system for video games that leverages user interaction data and attainment levels to improve personalized recommendations, addressing the unique aspects of gaming behavior.

Contribution

It introduces a novel graph-query recommendation approach utilizing attainment data and social information specific to video gaming.

Findings

Attainment data has high predictive value for recommendations.

Graph-query methods effectively incorporate social and interaction data.

Personalized recommendations benefit from gaming-specific features.

Abstract

The video game industry is larger than both the film and music industries combined. Recommender systems for video games have received relatively scant academic attention, despite the uniqueness of the medium and its data. In this paper, we introduce a graph-based recommender system that makes use of interactivity, arguably the most significant feature of video gaming. We show that the use of implicit data that tracks user-game interactions and levels of attainment (e.g. Sony Playstation Trophies, Microsoft Xbox Achievements) has high predictive value when making recommendations. Furthermore, we argue that the characteristics of the video gaming hobby (low cost, high duration, socially relevant) make clear the necessity of personalized, individual recommendations that can incorporate social networking information. We demonstrate the natural suitability of graph-query based recommendation for this purpose.