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Common structure and properties of filtering systems

机译：过滤系统的共同结构和特性

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摘要

Recommendation systems have been studied actively since the 1990s. Generally, recommendation systems choose one or more candidates from a set of candidates through a filtering process. Methods of filtering can be divided into two categories: collaborative filtering, in which candidates are chosen based on choices of other persons whose interests or tastes are similar, and content-based filtering, in which items are chosen based on the profile or action history of the recommendee. However, these methods share the same structure in the sense that both of them recommend items based on relevance degrees of items and references, as well as relevance degrees between the recommendee and each reference. Most discussions about recommendation systems focus on the methods of choosing recommended candidates; few focus on foundational concepts of recommendation conditions that systems must satisfy, and problems that current systems have compared with these conditions. In this paper, recommendation systems are reconsidered from the viewpoint of multi-criteria decision making. Conventional filtering methods (e.g., collaborative filtering and content-based filtering) are formulated as linear weighted sum type recommendation systems. Several properties of linear weighted sum type recommendation systems are identified and formulated from the viewpoint of voting.
机译：自1990年代以来，积极研究了推荐系统。通常，推荐系统通过过滤过程从一组候选者中选择一个或多个候选者。筛选方法可分为两类：协同筛选，其中基于兴趣或品味相似的其他人的选择来选择候选者，以及基于内容的筛选，其中基于用户的个人资料或动作历史来选择项目被推荐人。然而，在这两种方法都基于项目和参考的相关度以及被推荐者与每个参考之间的相关度来推荐项目的意义上，这些方法具有相同的结构。关于推荐系统的大多数讨论都集中在选择推荐候选人的方法上。很少有人关注系统必须满足的推荐条件的基本概念，以及当前系统与这些条件相比所存在的问题。本文从多准则决策的角度重新考虑了推荐系统。常规过滤方法（例如，协作过滤和基于内容的过滤）被表述为线性加权和类型推荐系统。从投票的角度，确定并制定了线性加权和类型推荐系统的一些属性。

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