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arXiv · 1808.05988

Attainment Ratings for Graph-Query Recommendation

Hal Cooper·Garud Iyengar·Ching-Yung Lin
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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.

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Hal Cooper, Garud Iyengar, Ching-Yung Lin. 2018-08-17. Attainment Ratings for Graph-Query Recommendation. https://arxiv.org/abs/1808.05988

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