An expressive framework and efficient algorithms for the analysis of collaborative tagging

Das, Mahashweta; Thirumuruganathan, Saravanan; Amer-Yahia, Sihem; Das, Gautam; Yu, Cong

doi:10.1007/s00778-013-0341-y

Cited by 14 publications

(14 citation statements)

References 39 publications

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“…In [5], the authors tackle the problem of rating interpretation by providing two methods (DEM, DIM). While the first one aims to discover groups of users that substantially agree for a given set of items, the second addresses the discovery of groups with an apparent inner discord.…”

Section: Related Workmentioning

confidence: 99%

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

Belfodil

Cazalens

Lamarre

et al. 2017

Machine Learning and Knowledge Discovery in Databases

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Section: Related Workmentioning

confidence: 99%

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

Belfodil

Cazalens

Lamarre

et al. 2017

Machine Learning and Knowledge Discovery in Databases

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“…In other words, if a contributor rides a bike he will see the street from a different perspective than if a contributor is in a wheeling chair. The second kind of task has been studied by (Das et al 2014) who experimented with a task assignment model to organize the production of one content among several contributors to optimize exhaustiveness, cost and precision. The production is modelled as a task decomposed into subtasks that can be assigned to people.…”

Section: Task Modellingmentioning

confidence: 99%

Enhancing the management of quality of VGI: contributions from context and task modelling

Bucher¹,

Falquet²,

Métral³

et al. 2016

European Handbook of Crowdsourced Geographic Information

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This chapter presents contributions to managing the quality of Volunteered Geographical Information (VGI) and of crowd sourced geographical information (CSGI) brought by the representation of specific knowledge items: task and context. Task and context modelling have been studied in different communities. We propose an approach for integrating their results with the perspective of improving the quality management of VGI and CSGI.

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“…Finding these kinds of relations has attracted much interest for unstructured data over the years, for instance finding the descriptions of users that consistently rate items in a certain way (Das et al 2011). Such unstructured settings can be challenging, for instance, when describing consistent behavior with respect to several target values (Das et al (2014)). More generally, finding descriptions that define subgroups of objects/entities for which the target variables exhibit unusual behavior compared to the entire data has been intensively studied in the field of subgroup discovery (SD) and exceptional model mining (EMM) (Leman et al 2008;Duivesteijn et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

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Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a χ 2 test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.

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An expressive framework and efficient algorithms for the analysis of collaborative tagging

Cited by 14 publications

References 39 publications

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

Flash Points: Discovering Exceptional Pairwise Behaviors in Vote or Rating Data

Enhancing the management of quality of VGI: contributions from context and task modelling

Exceptional contextual subgraph mining

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