Outlines of a Graph-Tensor Based Adaptive Associative Search Model for Internet of Digital Reality Applications

Tarek Setti, Á. Csapó
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Abstract

Internet of Digital Reality (IoD) is a technological vision that promises to radically transform existing digital ecosystems in a way that enables users to access all the content and capabilities - whether physical or digital - relevant to a goal-driven purpose in a highly integrated single environment. In this paper, we focus on a specific challenge that we expect will be crucial in making advances in this field: namely, the challenge of developing an effective search method that is personalized, adaptive and associative. As a possible solution to this challenge, we propose a graph-tensor based information model that incorporates the history of search keywords and inferred associations between them across potentially multiple search dimensions. We provide a brief discussion on why we assume this model to have advantageous properties and provide a short use-case example to motivate further research.
数字现实互联网应用中基于图张量的自适应关联搜索模型概述
数字现实互联网(IoD)是一种技术愿景,它承诺从根本上改变现有的数字生态系统,使用户能够在高度集成的单一环境中访问与目标驱动目的相关的所有内容和功能(无论是物理的还是数字的)。在本文中,我们将重点关注一个特定的挑战,我们预计这将是在这一领域取得进展的关键:即,开发一种个性化、自适应和联想的有效搜索方法的挑战。作为应对这一挑战的一种可能的解决方案,我们提出了一种基于图张量的信息模型,该模型结合了搜索关键字的历史,并在潜在的多个搜索维度上推断出它们之间的关联。我们提供了一个简短的讨论,说明为什么我们假设这个模型具有有利的属性,并提供了一个简短的用例示例来激励进一步的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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