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A similarity metric designed to speed up, using hardware,the recommender systems k-nearest neighbors algorithm

机译：一种相似度量，旨在使用硬件加速推荐系统的k近邻算法

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摘要

A significant number of recommender systems utilize the k-nearest neighbor (kNN) algorithm as the collaborative filtering core. This algorithm is simple; it utilizes updated data and facilitates the explanations of recommendations. Its greatest inconveniences are the amount of execution time that is required and the non-scalable nature of the algorithm. The algorithm is based on the repetitive execution of the selected similarity metric. In this paper, an innovative similarity metric is presented: HwSimilarity. This metric attains high-quality recommendations that are similar to those provided by the best existing metrics and can be processed by employing low-cost hardware circuits. This paper examines the key design concepts and recommendation-quality results of the metric. The hardware design, cost of implementation, and improvements achieved during execution are also explored.
机译：大量推荐系统将k最近邻居（kNN）算法用作协作过滤核心。这个算法很简单；它利用更新的数据并简化了建议的解释。其最大的不便是所需的执行时间量和算法的不可伸缩性。该算法基于所选相似性度量的重复执行。本文提出了一种创新的相似性度量标准：HwSimilarity。此度量标准获得的高质量建议与现有最佳度量标准所提供的建议相似，可以通过使用低成本硬件电路进行处理。本文研究了该指标的关键设计概念和推荐质量结果。还探讨了硬件设计，实现成本以及在执行过程中实现的改进。

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