Multi-instance multi-label learning for automatic tag recommendation

Shen, Chen; Jiao, Jun; Yang, Yanxi; Wang, Bin

doi:10.1109/icsmc.2009.5346261

Cited by 14 publications

(14 citation statements)

References 13 publications

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“…113, the problem of suggesting tags is modeled as a multilabel text classification task, using a widelyused problem transformation method known as binary relevance, and it is used to classify items from Bibsonomy. Shen et al 114 approach divides each Web page into many sections or bag of instances using a document segmentation algorithm and then uses multiinstance, multilabel support vector machines (MIMLSVM) to learn the relationship between labels and bags. In their prototype-based method for tag recommendation, Song et al 115 train a multiclass, multilabel Gaussian process classifiers to later find the most informative prototypes for each class.…”

Section: Classification Methodsmentioning

confidence: 99%

Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Godoy

Corbellini

2015

Int. J. Intell. Syst.

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Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and linkanalysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines. C 2015 Wiley Periodicals, Inc.

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Section: Classification Methodsmentioning

confidence: 99%

Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Godoy

Corbellini

2015

Int. J. Intell. Syst.

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“…As a matter of fact, the En-MIMLSVM is a new approach for the video annotation task [18]. The MIML learning is used for automatic tag recommendation and each Web page is divided into a bag of instances [19]. For automatic object detection tasks, a large number of training images are labeled by a multi-task multi-label multi-instance learning (MTML-MIL) [20].…”

Section: Q4mentioning

confidence: 99%

Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

Abdechiri

Faez

2015

Applied Soft Computing

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“…Another learning problem is multi-instance multilabel learning, where each object is described by a bag of instances and is assigned a set of labels [17]. Different real-world applications can be handled under this framework.…”

Section: Related Workmentioning

confidence: 99%

A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule

Younes

Abdallah

Denœux

et al. 2011

EURASIP J. Adv. Signal Process.

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In multilabel classification, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly used approach for multilabel classification is where a binary classifier is learned independently for each possible class. However, multilabeled data generally exhibit relationships between labels, and this approach fails to take such relationships into account. In this paper, we describe an original method for multilabel classification problems derived from a Bayesian version of the k-nearest neighbor (k-NN) rule. The method developed here is an improvement on an existing method for multilabel classification, namely multilabel k-NN, which takes into account the dependencies between labels. Experiments on simulated and benchmark datasets show the usefulness and the efficiency of the proposed approach as compared to other existing methods.

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Multi-instance multi-label learning for automatic tag recommendation

Cited by 14 publications

References 13 publications

Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

A Dependent Multilabel Classification Method Derived from the k-Nearest Neighbor Rule

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