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Methods and algorithms for service selection and recommendation (preference and aggregation based).

机译：用于服务选择和推荐的方法和算法（基于偏好和聚合）。

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摘要

In order for service users to get the best service that meets their requirements, they prefer to personalize their non-functional attributes, such as reliability and price. However, the personalization makes it challenging because service providers have to deal with conflicting non-functional attributes when selecting services for users. In addition, users may sometimes want to explicitly specify their trade-offs among non-functional attributes to make their preferences known to service providers. Typically, users' service search requests with conflicting non-functional attributes may result in a ranked list of services that partially meet their needs. When this happens, it is natural for users to submit other similar requests, with varying preferences on non-functional attributes, in an attempt to find services that fully meet their needs. This situation produces a challenge for the users to choose an optimal service based on their preferences, from the multiple ranked lists that partially satisfy their request.;Existing memory-based collaborative filtering (CF) service recommendation methods that employ this recommendation technique usually depend on non-functional attribute values obtained at service invocation to compute the similarity between users or items, and also to predict missing non-functional attributes. However, this approach is not sufficient because the non-functional attribute values of invoked services may not necessarily satisfy their personalized preferences.;The main contributions of this work are threefold. First, a novel service selection method, which is based on fuzzy logic, that considers users' personalized preferences and their trade-offs on non-functional attributes during service selection is presented. Second, a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user is also presented. Two algorithms were proposed: 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Finally, a CF-based service recommendation method that considers users' personalized preference on non-functional attributes if proposed. Examples using real-world services are presented to evaluate the proposed methods and experiments are carried out to validate their performance.
机译：为了使服务用户能够获得满足其要求的最佳服务，他们倾向于个性化其非功能性属性，例如可靠性和价格。但是，个性化使其具有挑战性，因为服务提供商在为用户选择服务时必须处理冲突的非功能属性。另外，用户有时可能希望在非功能属性之间明确指定他们的取舍，以使服务提供商知道其偏好。通常，具有冲突的非功能属性的用户服务搜索请求可能会导致部分满足其需求的服务排名。发生这种情况时，用户自然会提交其他类似的请求，对非功能属性的偏好会有所不同，以尝试找到完全满足其需求的服务。这种情况给用户带来了挑战，即用户需要根据自己的偏好从部分满足其要求的多个排名列表中选择最佳服务。现有的采用此推荐技术的基于内存的协同过滤（CF）服务推荐方法通常取决于服务调用时获得的非功能属性值，以计算用户或项目之间的相似度，并预测丢失的非功能属性。但是，这种方法是不够的，因为被调用服务的非功能属性值可能不一定满足其个性化偏好。这项工作的主要贡献是三方面的。首先，提出了一种基于模糊逻辑的新型服务选择方法，该方法考虑了用户的个性化偏好及其在服务选择过程中对非功能性属性的取舍。其次，还提出了一种方法，该方法将多个排序的服务列表聚合到单个聚合的排序列表中，在其中为用户选择了排名最高的服务。提出了两种算法：1）完整列表的排名聚合（RACoL），用于聚合完整排名列表； 2）不完整列表的排名聚合（RAIL）以聚合不完整排名列表。最后，提出了一种基于CF的服务推荐方法，该方法考虑用户对非功能属性的个性化偏好。给出了使用现实世界服务的示例来评估所提出的方法，并进行了实验以验证其性能。

著录项

  • 作者

    Fletcher, Kenneth Kofi.;

  • 作者单位

    Missouri University of Science and Technology.;

  • 授予单位 Missouri University of Science and Technology.;
  • 学科 Computer science.;Web studies.;Information science.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 108 p.
  • 总页数 108
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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