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CONVERGENCE RANK AND ITS APPLICATIONS

机译：收敛等级及其应用

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摘要

In this paper, we explore an algorithm to determine the relevance of each item in a finite set of items in reference to each other to address an item you have to first go through a convergence or proxy item. If we imagine a media streaming company (convergence item) and all its available genres for playback (items in a finite set), how relevant is each music genre at different moments in time? Or with a sports broadcasting company and the covered sports, how does the relevance of each sport changes throughout the year as sports seasons begin and end? As the algorithm gets developed in the paper, we introduce an artificial node to the relationship graph, which brings a disproportional weight in importance. Later, we show how to remove the artificial node from the final rank vector to obtain a ranking of items in the set without any distortions. In addition to the ranking of a single point in time, the algorithm expands to analyze a sequence of consecutive convergence rankings, infers the behavior of trends and allows for the forecasting of near-future or cyclical ranks. The applications of this algorithm are immediate and plentiful in possibilities. In its essence, this algorithm can help to understand the usage behavior of services by its users based on non-invasive simple data collection. From its results, it is possible to better plan the allocation of resources.
机译：在本文中，我们探索了一种算法，该算法可以确定有限组项目中每个项目之间的相互关联性，以解决您必须首先通过会聚或代理项目进行处理的项目。如果我们想象一家媒体流媒体公司（会聚项目）及其所有可播放类型（有限集合中的项目），那么每种音乐类型在不同时刻的相关性如何？还是与体育广播公司和所涵盖的体育节目一起，随着体育季节的开始和结束，每种体育活动的相关性在一年中如何变化？随着本文算法的发展，我们在关系图中引入了人工节点，这在重要性上具有不成比例的权重。稍后，我们将展示如何从最终等级向量中删除人工节点，以在不失真的情况下获得集合中项目的等级。除了对单个时间点进行排名之外，该算法还可以扩展以分析一系列连续的收敛排名，推断趋势的行为，并允许预测未来或周期性排名。该算法的应用是直接且可能的。从本质上讲，该算法可以帮助用户基于非侵入性简单数据收集来了解其用户的服务使用行为。根据其结果，可以更好地计划资源分配。

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