Controlling the crucible: a novel PvP recommender systems framework for destiny

ABSTRACT

Compared to conventional retail games, today's Massively Multiplayer Online Games (MMOGs) have become progressively more complex and volatile, living in a highly competitive market. Consumable resources in such games are nearly unlimited, making decisions to improve levels of engagement more challenging. Intelligent information filtering methods here can help players make smarter decisions, thereby improving performance, increasing level of engagement, and reducing the likelihood of early departure. In this paper, a novel approach towards building a hybrid multi-profile based recommender system for player-versus-player (PvP) content in the MMOG Destiny is presented. The framework groups the players based on three distinct traced behavioral aspects: base stats, cooldown stats, and weapon playstyle. Different combinations of these profiles are considered to make behavioral recommendations. An online evaluation was performed to investigate the usefulness of the proposed recommender framework to players of Destiny.

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Index Terms

• Applied computing

  • Computers in other domains

    • Personal computers and PC applications

      • Computer games

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification