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Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation

: Sifa, Rafet; Yawar, Raheel; Ramamurthy, Rajkumar; Bauckhage, Christian

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Association for the Advancement of Artificial Intelligence -AAAI-:
Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2018. Proceedings : Edmonton, Alberta, Canada, November 13-17, 2018
Menlo Park: AAAI Press, 2018
Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE) <14, 2018, Edmonton>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
Game Content Recommender Systems; behavioral analytics; Matrix and Tensor Factorization; collaborative filtering; Player Modeling; Player Performance Metrics; retention analysis; online evaluation

Players of digital games face numerous choices as to what kind of games to play and what kind of game content or in-game activities to opt for. Among these, game content plays an important role in keeping players engaged so as to increase revenues for the gaming industry. However, while nowadays a lot of game content is generated using procedural content generation, automatically determining the kind of content that suits players' skills still poses challenges to game developers. Addressing this challenge, we present matrix- and tensor factorization based game content recommender systems for recommending quests in a single player role-playing game. We discuss the theory behind latent factor models for recommender systems and derive an algorithm for tensor factorizations to decompose collections of bipartite matrices. Extensive online bucket type tests reveal that our novel recommender system retained more players and recommended more engaging quests than handcrafted content-based and previous collaborative filtering approaches.