Studying Recommendation Algorithms by Graph Analysis

BATUL J. MIRZA; BENJAMIN J. KELLER; NAREN RAMAKRISHNAN

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Studying Recommendation Algorithms by Graph Analysis

机译：通过图分析研究推荐算法

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摘要

We present a novel framework for studying recommendation algorithms in terms of the 'jumps' that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a reeommender dataset and allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm 'jump,' what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the 'hammock' using movie reeommender datasets.

机译：我们提出了一个新颖的框架，用于研究推荐算法将人与工件联系起来的“跳跃”。这种方法通过在reeommender数据集下面的隐式图结构内的算法来强调可达性，并允许我们考虑将算法参数与数据集的属性相关的问题。例如，给定特定的算法“跳跃”，从人到工件的平均路径长度是多少？或者，最小额定值和跳跃的哪些选择可以保持连接的图形？我们使用电影reeommender数据集以一个称为“吊床”的常见跳转来说明该方法。

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来源
《Journal of Intelligent Information Systems》 |2003年第2期|p.131-160|共30页
作者
BATUL J. MIRZA; BENJAMIN J. KELLER; NAREN RAMAKRISHNAN;
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作者单位

Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
reeommender systems; collaborative filtering; information system evaluation; random graphs;

机译：reeommender系统;协同过滤;信息系统评估;随机图;

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Studying Recommendation Algorithms by Graph Analysis

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