• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Information Modelling and Knowledge Bases >Responsive Calibrated Web Personalization System with Online Local Variational Inference for the Logistic Regression Mixture Model
【24h】

Responsive Calibrated Web Personalization System with Online Local Variational Inference for the Logistic Regression Mixture Model

机译：响应校准的Web个性化系统，具有在线局部变分推理的Logistic回归混合模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Improved computing environments performing large-scale data processing and high-speed computational processing facilitate the delivery of new algorithms to businesses while considering cost efficiency for small-scale investments. Implementing the proposed method more as a criterion for feasibility and economic rationality in specific problem areas rather than as an approach to generic issues, we aim to develop technologies of practical use in the real world. Recently, it has become possible for customers to monitor their buying behavior through smart devices, and with the improvement of computing performance, it has become possible to improve the accuracy of prediction and recommendation cycles through active online learning. This study proposes a method for dynamically recommending products that are highly likely to be selected by the user by combining the user's reaction with reuse of knowledge and real-time online learning to cyclically repeat feedback that is more specific to the user. We propose a method to sense streaming data by utilizing a user's behavior, intervening a user's behavioral change through interactions, such as recommendations, and evaluating the user's buying intention and interest in each product. Using the evaluation results for recommendations helps achieve positive feedback and effectively support the selection of more exciting or different products. We propose a recommendation method specific to individual customers based on past transaction data, where changes can be monitored in real-time by reusing the knowledge acquired in advance through batch processing of knowledge discovery and data mining and processing the stream data in real-time online. We will present the implementation of our proposed method targeting the database system and machine learning algorithm.
机译：执行大规模数据处理和高速计算处理提高计算环境有利于为企业交付新的算法，同时考虑小规模投资的成本效益。实现所提出的方法更为具体问题领域的，而不是作为一种方法来一般性问题的可行性和经济合理性的标准，我们的目标是制定切实可行的利用技术在现实世界中。最近，它已成为客户能够通过监控智能设备他们的购买行为，并与计算性能的提高，它已成为可能通过活跃的在线学习，以提高预测和推荐周期的准确性。这项研究提出了建议动态那些极有可能由用户通过将用户与知识，并实时在线学习循环重复的反馈，更具体的用户重用反应选中的产品的方法。我们提出了一个方法来检测通过利用用户的行为，通过介入的相互作用，例如建议用户的行为变化，并评估用户的购买意愿并在每个产品的兴趣流数据。使用评价结果的建议有助于实现正反馈，有效地支持更多精彩或不同产品的选择。我们提出了一个建议，方法具体到基于过去的交易数据，其中的变化可以实时通过重用通过知识发现和数据挖掘的批量处理预先获得的知识和在线处理实时流数据来监控个人客户。我们将目前我们提出的方法针对数据库系统和机器学习算法的实现。

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