• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >DUSKG: A fine-grained knowledge graph for effective personalized service recommendation
【24h】

DUSKG: A fine-grained knowledge graph for effective personalized service recommendation

机译：DUSKG：细粒度的知识图，可提供有效的个性化服务推荐

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 last decade has witnessed the rapid development of service-oriented computing, resulting in a tremendous growth of services which further calls for novel approaches for efficient and effective service recommendations. Various types of data can be used for service recommendations, such as user feedback on services, service profile, user profile, etc. In traditional recommendation algorithms, such data is usually handled separately and in the form of matrix. Since there are underlying logical relations between the data, we conjecture that deep convergence of all the data and full considerations of such logical relations would help boost accuracy of service recommendations. By a comprehensive survey on various existing data convergence methods, we found that knowledge graph had been a typical approach to describe the different relations between data. In this paper, we propose an elegant, natural and compact data representation model incorporating such kinds of logical relations, namely Domain-oriented User and Service Interaction Knowledge Graph (DUSKG). Three types of entities (User, Service and Service Value Feature(VF)), five types of fine-grained relations (FOCUSON, BELONGTO, USIMILAR, SSIMILAR and FSIMILAR), and weight vectors that quantitatively delineate these relations are elaborately defined. An approach for constructing DUSKG from service data is presented. Extracted from DUSKG, each user's value preference is then represented in terms of VFs. Five personalized service recommendation methods are presented in terms of such value preferences and various relations in DUSKG. To verify the proposed methods, a set of experiments are conducted on Yelp dataset. Experimental results show that our approaches achieve better recommendation performance compared to the state-of-the-art methods, and among the five ones, HR-DUSKG (Hybrid Recommendation Depending on DUSKG) exhibits the best performance with quite good achievements but least calculation time. (C) 2019 Elsevier B.V. All rights reserved.
机译：过去十年见证了面向服务的计算的飞速发展，从而导致服务的巨大增长，这进一步要求采用新颖的方法来提供高效，有效的服务建议。各种类型的数据可用于服务推荐，例如用户对服务的反馈，服务配置文件，用户配置文件等。在传统推荐算法中，此类数据通常以矩阵形式单独处理。由于数据之间存在潜在的逻辑关系，我们推测所有数据的深入融合以及对这种逻辑关系的充分考虑将有助于提高服务建议的准确性。通过对各种现有数据收敛方法的全面调查，我们发现知识图已经成为描述数据之间不同关系的一种典型方法。在本文中，我们提出了一种优雅，自然，紧凑的数据表示模型，该模型结合了此类逻辑关系，即面向领域的用户和服务交互知识图（DUSKG）。精心定义了三种类型的实体（用户，服务和服务价值特征（VF）），五种精细关系（FOCUSON，BELONGTO，USIMILAR，SSIMILAR和FSIMILAR）以及定量描述这些关系的权重向量。提出了一种从服务数据构建DUSKG的方法。从DUSKG中提取的每个用户的价值偏好都以VF表示。根据这种价值偏好和DUSKG中的各种关系，提出了五种个性化服务推荐方法。为了验证所提出的方法，对Yelp数据集进行了一组实验。实验结果表明，与最先进的方法相比，我们的方法具有更好的推荐性能，在这五种方法中，HR-DUSKG（取决于DUSKG的混合推荐）表现出最好的性能，并且效果很好，但计算时间最少。 （C）2019 Elsevier B.V.保留所有权利。

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