Learning consumer preferences using semantic similarity
Learning consumer preferences using semantic similarity
复制标题
使用语义相似性学习消费者偏好
DOI：
10.1145/1329125.1329401
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
P. Yolum
中科院分区：
文献类型：
--
作者：
Reyhan Aydoğan;P. Yolum
在在线动态环境中，提供者可能不容易为消费者所请求的服务提供服务。这需要服务使用者和提供者协商他们的服务需求和提供。多智能体谈判方法通常假设各方就服务内容达成一致，并专注于在服务价格上达成共识。相反，这项工作开发了一种方法，各方可以通过这种方法协商服务的内容。这需要一种谈判方法，在这种方法中，各方可以理解他们的请求和提议的语义，并随着时间的推移逐渐了解彼此的偏好。因此，我们提出了一个消费者和生产者都使用共享本体来协商服务的体系结构。通过重复的互动，供应商准确地了解消费者的需求，并能够提供更有针对性的报价。为了能够快速准确地学习偏好，我们开发了对版本空间的扩展，并将其与现有的学习技术进行了比较。我们进一步开发了一个度量服务之间语义相似度的度量，并使用不同的相似度度量比较了我们方法的性能。
In online, dynamic environments, the services requested by consumers may not be readily served by the providers. This requires the service consumers and providers to negotiate their service needs and offers. Multiagent negotiation approaches typically assume that the parties agree on service content and focus on finding a consensus on service price. In contrast, this work develops an approach through which the parties can negotiate the content of a service. This calls for a negotiation approach in which the parties can understand the semantics of their requests and offers and learn each other's preferences incrementally over time. Accordingly, we propose an architecture in which both consumers and producers use a shared ontology to negotiate a service. Through repetitive interactions, the provider learns consumers' needs accurately and can make better targeted offers. To enable fast and accurate learning of preferences, we develop an extension to Version Space and compare it with existing learning techniques. We further develop a metric for measuring semantic similarity between services and compare the performance of our approach using different similarity metrics.