论文标题
基于内容的玩家和游戏互动模型在冷启动设置中推荐
Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting
论文作者
论文摘要
游戏建议是推荐系统的重要应用。历史玩家和游戏互动的数据集使建议成为可能，有时数据集包括描述游戏或玩家的功能。已发现协作过滤是过去相互作用的最准确的预测指标。但是，它只能用于预测存在大量过去互动的游戏和玩家的新互动。换句话说，对全新游戏和玩家的预测是不可能的。在本文中，我们使用的是游戏的调查数据集喜欢提出基于内容的交互模型，这些模型可以同时推广到新游戏，新玩家以及新游戏和玩家。我们发现这些模型在这些任务中的合作过滤效果优于协作，这使它们对于现实世界游戏的推荐有用。内容模型还提供了解释为什么某些玩家喜欢某些游戏以用于游戏分析目的。
Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the data sets include features that describe games or players. Collaborative filtering has been found to be the most accurate predictor of past interactions. However, it can only be applied to predict new interactions for those games and players where a significant number of past interactions are present. In other words, predictions for completely new games and players is not possible. In this paper, we use a survey data set of game likes to present content based interaction models that generalize into new games, new players, and both new games and players simultaneously. We find that the models outperform collaborative filtering in these tasks, which makes them useful for real world game recommendation. The content models also provide interpretations of why certain games are liked by certain players for game analytics purposes.
