摘要翻译:
本文提出了一种基于Kalman滤波的目标跟踪推荐系统的新方法。我们假设用户和他们看到的资源是资源类别的多维空间中的向量。了解了这个空间,我们提出了一个基于Kalman滤波器的算法来跟踪用户,并预测他们在推荐空间中未来位置的最佳预测。
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英文标题:
《A new Recommender system based on target tracking: a Kalman Filter
approach》
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作者:
Samuel Nowakowski (LORIA), C\'edric Bernier (LORIA), Anne Boyer
(LORIA)
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最新提交年份:
2010
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
In this paper, we propose a new approach for recommender systems based on target tracking by Kalman filtering. We assume that users and their seen resources are vectors in the multidimensional space of the categories of the resources. Knowing this space, we propose an algorithm based on a Kalman filter to track users and to predict the best prediction of their future position in the recommendation space.
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PDF链接:
https://arxiv.org/pdf/1012.3280