上下文感知推荐系统的用户元模型

HetRec '10 Pub Date : 2010-09-26 DOI:10.1145/1869446.1869456
Jon Imanol Durán, J. Laitakari, D. Pakkala, Juho Perälä
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引用次数: 10

摘要

用户配置文件越来越多地用于在上下文感知代理之间共享关于用户的标准信息。用户配置文件允许代理为用户提供个性化的内容和服务。但是,这些代理使用的实体和上下文信息必须具有相同的含义,以便对用户相关的个人信息、上下文和偏好有共同的理解。本文的贡献在于提出了一个通用的用户元数据模型,该模型集成在一个通用元数据模型(CAM元模型)中,该模型涵盖了有关内容、服务、物理和技术环境的全部信息。设计这个新的用户概要元模型的目的是将其与内容和服务推荐系统结合使用。它带来了对用户上下文数据进行推理的新机会,其主要目的是在无处不在的环境中增加用户体验,并根据具体情况满足他们的需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A user meta-model for context-aware recommender systems
User profiles are increasingly used for sharing standard information about users among context-aware agents. User profiles allow agents to offer users personalized content and services. However, the entities and contextual information used by these agents must have same meaning in order to share a common understanding about user related personal information, context and preferences. The contribution of this paper is to present a general user metadata model which is integrated within a generic metadata model (CAM Meta-model) that covers altogether information about content, services, physical and technical environment. This new user profile meta-model has been designed with a view of using it in conjunction with content and service recommender systems. It brings new opportunities to reason over user context data with the main purpose of increasing user experience in ubiquitous environments and satisfying their desires depending on the circumstances.
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