• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2018 IEEE Intl Conf on Parallel amp; Distributed Processing with Applications, Ubiquitous Computing amp; Communications, Big Data amp; Cloud Computing, Social Computing amp; Networking, Sustainable Computing amp; Communications >Hierarchical Attention Based Recurrent Neural Network Framework for Mobile MOBA Game Recommender Systems
【24h】

Hierarchical Attention Based Recurrent Neural Network Framework for Mobile MOBA Game Recommender Systems

机译：基于分层注意力的移动MOBA游戏推荐系统递归神经网络框架

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The mobile multiplayer online battle arena (MOBA) game is a genre of real-time strategy video games on mobile devices, such as King of Glory. The main business model is to drive players to purchase items like heroes or skins. Recommending items based on player interest is the core task of recommender systems. In the MOBA game, player interest changes over the game experience, which is implied in player behavior based on historical game matches. Match sequences, that consist of every match in the timeline, indicate how players interact with the game and the change process of player interest. Recurrent neural networks (RNNs) are employed by many recommendation scenes to model sequence data to profile user preference for better recommendation accuracy. However, their RNNs based frameworks ignore the interpretability of recommendation results, which is an important requirement for mobile MOBA games. To solve this challenge, we propose an interpretable RNN framework based on hierarchical attention in this work, which is inspired by the attention mechanism applied in machine translation. The main component long short-term memory (LSTM), that is the RNN variant, models player interest from historical match sequences, and the hierarchical attention is used to measure the effect factors of matches and behavior events happened in a match. To verify effectiveness, we train several models on real mobile MOBA game King of Glory datasets. Compared to non-sequence models, our model achieves 2% higher accuracy; with hierarchical attention, the proposed model can interpret the recommendation results effectively compared to naive RNN based models.
机译：移动多人在线战斗竞技场（MOBA）游戏是在诸如荣耀之王之类的移动设备上的实时战略视频游戏类型。主要的商业模式是促使玩家购买英雄或皮肤之类的物品。根据玩家的兴趣推荐项目是推荐系统的核心任务。在MOBA游戏中，玩家的兴趣随游戏体验而变化，这暗示着基于历史游戏比赛的玩家行为。比赛序列由时间轴上的每个比赛组成，表明玩家如何与游戏互动以及玩家兴趣的变化过程。许多推荐场景都使用递归神经网络（RNN）对序列数据进行建模，以分析用户的偏好，以获得更好的推荐准确性。但是，他们基于RNN的框架忽略了推荐结果的可解释性，这是移动MOBA游戏的重要要求。为了解决这一挑战，我们在这项工作中提出了一个基于层次化注意力的可解释的RNN框架，该框架受到机器翻译中应用的注意力机制的启发。 RNN的主要组成部分是长期短期记忆（LSTM），它根据历史比赛序列来模拟玩家的兴趣，分层注意力用于衡量比赛的影响因素以及比赛中发生的行为事件。为了验证有效性，我们在真实的移动MOBA游戏King of Glory数据集中训练了几种模型。与非序列模型相比，我们的模型可将精度提高2％。与单纯的基于RNN的模型相比，该模型具有层次结构的关注，可以有效地解释推荐结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号