Determining the Impact Regions of Competing Options in Preference Space

Tang, Bo; Mouratidis, Kyriakos; Yiu, Man Lung

doi:10.1145/3035918.3064044

Cited by 20 publications

(4 citation statements)

References 31 publications

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“…Such conditions cannot restrain user preferences, because score ranking depends only on the direction of w instead of its magnitude [19]; but they allow w to drop one weight (i.e., w d = 1 − d−1 i=1 w i ), thereby mapping the domain of w to a (d − 1)-dimensional space, named the preference domain. This dimensionality reduction is critical [20], since the dimensionality directly determines the processing time of the costliest operations in our techniques. In the following, w refers to the (d−1)-dimensional form of the weight vector.…”

Section: Score Of Multi-attributesmentioning

confidence: 99%

Multi-attributed Community Search in Road-social Networks

Guo,

Yuan,

Wang

et al. 2021

Preprint

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Given a location-based social network, how to find the communities that are highly relevant to query users and have top overall scores in multiple attributes according to user preferences? Typically, in the face of such a problem setting, we can model the network as a multi-attributed road-social network, in which each user is linked with location information and d (≥ 1) numerical attributes. In practice, user preferences (i.e., weights) are usually inherently uncertain and can only be estimated with bounded accuracy, because a human user is not able to designate exact values with absolute precision. Inspired by this, we introduce a normative community model suitable for multi-criteria decision making, called multi-attributed community (MAC), based on the concepts of k-core and a novel dominance relationship specific to preferences. Given uncertain user preferences, namely, an approximate representation of weights, the MAC search reports the exact communities for each of the possible weight settings. We devise an elegant index structure to maintain the dominance relationships, based on which two algorithms are developed to efficiently compute the top-j MACs. The efficiency and scalability of our algorithms and the effectiveness of MAC model are demonstrated by extensive experiments on both realworld and synthetic road-social networks.

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Section: Score Of Multi-attributesmentioning

confidence: 99%

Multi-attributed Community Search in Road-social Networks

Guo,

Yuan,

Wang

et al. 2021

Preprint

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“…Multi-preference recommendation and multi-criteria decision making based on top-k query processing has become a hot topic and has been studied extensively in the last two decades. In the context of multi criteria top-k query processing, computational geometry driven top-k query processing models have gained a lot of interest in recent years [11,12,13,14,15] and could be successfully adapted to several variants of top-k queries in multi-criteria settings [12]. The principal idea of the above mentioned line of work is to translate object domination according to multi-criteria preferences into hyperplane bisection of the multidimensional preference space.…”

Section: Multi-preference Recommendationmentioning

confidence: 99%

Complete and Sufficient Spatial Domination of Multidimensional Rectangles

Emrich¹,

Kriegel²,

Züfle³

et al. 2020

Preprint

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Rectangles are used to approximate objects, or sets of objects, in a plethora of applications, systems and index structures. Many tasks, such as nearest neighbor search and similarity ranking, require to decide if objects in one rectangle A may/must/must not be closer to objects in a second rectangle B, than objects in a third rectangle R. To decide this relation of "Spatial Domination" it can be shown that using minimum and maximum distances it is often impossible to detect spatial domination. This spatial gem provides a necessary and sufficient decision criterion for spatial domination that can be computed efficiently even in higher dimensional space. In addition, this spatial gem provides an example, pseudocode and an implementation in Python.

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“…Recent work also supports time series data [32] or avoids false discoveries of statistical patterns [44] during interactive data exploration. Finding best database objects based on user preferences [40] assumes a numeric weight per database attribute, which is di↵erent from the active learning approach to discover the user interest on the fly. Query by Example is a specific framework for data exploration.…”

Section: Related Workmentioning

confidence: 99%

Optimization for active learning-based interactive database exploration

et al. 2018

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et al.. Optimization for active learning-based interactive database exploration. ABSTRACTThere is an increasing gap between fast growth of data and limited human ability to comprehend data. Consequently, there has been a growing demand of data management tools that can bridge this gap and help the user retrieve highvalue content from data more e↵ectively. In this work, we aim to build interactive data exploration as a new database service, using an approach called "explore-by-example". In particular, we cast the explore-by-example problem in a principled "active learning" framework, and bring the properties of important classes of database queries to bear on the design of new algorithms and optimizations for active learning-based database exploration. These new techniques allow the database system to overcome a fundamental limitation of traditional active learning, i.e., the slow convergence problem. Evaluation results using real-world datasets and user interest patterns show that our new system significantly outperforms state-of-the-art active learning techniques and data exploration systems in accuracy while achieving desired e ciency for interactive performance.

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Determining the Impact Regions of Competing Options in Preference Space

Cited by 20 publications

References 31 publications

Multi-attributed Community Search in Road-social Networks

Multi-attributed Community Search in Road-social Networks

Complete and Sufficient Spatial Domination of Multidimensional Rectangles

Optimization for active learning-based interactive database exploration

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