Finding superior skyline points for multidimensional recommendation applications
Finding superior skyline points for multidimensional recommendation applications
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DOI：
10.1007/s11280-011-0122-8
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发表时间：
2011
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影响因子：
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通讯作者：
Jing Yang;G. Fung;Wei Lu;Xiaofang Zhou;Hong Chen;Xiaoyong Du
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文献类型：
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作者：
Jing Yang;G. Fung;Wei Lu;Xiaofang Zhou;Hong Chen;Xiaoyong Du
在一个典型的Web推荐系统中，对象通常由多个属性描述。它还需要为许多具有多样化偏好的用户提供服务。换句话说，它必须能够有效地支持高维偏好查询，允许用户有效地探索数据空间，而无需为每个维度施加特定的偏好权重。天际线查询，它可以产生一组对象，保证包含所有排名靠前的对象的任何线性属性偏好组合，已被提出来支持这种类型的推荐应用程序。然而，它遭受的问题被称为“维数灾难”的天际查询结果集的大小可以指数增长的维数。因此，当维数很高时，很大比例的对象可以成为天际线点。这个问题使得这样的推荐系统对于用户来说不太可用。在本文中，我们提出了一种更强的天际线查询，称为核心天际线查询，采用了一种新的质量度量称为垂直优势，只返回一个有趣的子集的传统天际线点。提出了一种高效的查询处理方法，利用一种新的索引结构Linked Multiple B '-Trees（LMB）来查找核心Skyline点。我们的方法可以逐步找到这样的上级天际线点，而不需要首先计算整个天际线点集。
In a typical Web recommendation system, objects are often described by many attributes. It also needs to serve many users with a diversified range of preferences. In other words, it must be capable to efficiently support high dimensional preference queries that allow the user to explore the data space effectively without imposing specific preference weightings for each dimension. The skyline query, which can produce a set of objects guaranteed to contain all top ranked objects for any linear attribute preference combination, has been proposed to support this type of recommendation applications. However, it suffers from the problem known as ‘dimensionality curse’ as the size of skyline query result set can grow exponentially with the number of dimensions. Therefore, when the dimensionality is high, a large percentage of objects can become skyline points. This problem makes such a recommendation system less usable for users. In this paper, we propose a stronger type of skyline query, calledcore skyline query, that adopts a new quality measure calledvertical dominanceto return only aninterestingsubset of the traditional skyline points. An efficient query processing method is proposed to find core skyline points using a novel indexing structure calledLinked Multiple B’-trees(LMB). Our approach can find such superior skyline points progressively without the need of computing the entire set of skyline points first.