Abstract
The video game industry is larger than both the film and music industries combined yet has received scant academic attention. We explore recommendations that makes use of interactivity, arguably the most distinguishing feature of video game products. We show that implicit data that tracks user-game interactions and levels of attainment (e.g. 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 tackle this problem from the viewpoint of graph querying and demonstrate the foundation of a new approach for learning structured graph queries from data.
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Cooper, H.J., Iyengar, G., Lin, CY. (2019). Personalized Product Recommendation for Interactive Media. In: Karwowski, W., Ahram, T. (eds) Intelligent Human Systems Integration 2019. IHSI 2019. Advances in Intelligent Systems and Computing, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-030-11051-2_77
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DOI: https://doi.org/10.1007/978-3-030-11051-2_77
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