A survey on tag recommendation methods

Belém, Fabiano; Almeida, Jussara M.; Gonçalves, Marcos André

doi:10.1002/asi.23736

Cited by 58 publications

(43 citation statements)

References 70 publications

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“…Examples of hybrid taxonomy-folksonomy approaches exist in the literature [ 43 , 44 ]. We might also assist users to tag content by providing recommendations generated through a variety of techniques, including tag cooccurrence, content-based, graph-based, and clustering- or topic-based methods [ 45 ]. However, it is important to consider whether the provision of tags might stifle creativity and prevent users from making a greater effort to fully elucidate their thoughts using the most appropriate tag.…”

Section: Discussionmentioning

confidence: 99%

The User Knows What to Call It: Incorporating Patient Voice Through User-Contributed Tags on a Participatory Platform About Health Management

Chen¹,

Carriere²,

Kaplan³

2017

J Med Internet Res

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BackgroundBody listening, described as the act of paying attention to the body’s signals and cues, can be an important component of long-term health management.ObjectiveThe aim of this study was to introduce and evaluate the Body Listening Project, an innovative effort to engage the public in the creation of a public resource—to leverage collective wisdom in the health domain. This project involved a website where people could contribute their experiences of and dialogue with others concerning body listening and self-management. This article presents an analysis of the tags contributed, with a focus on the value of these tags for knowledge organization and incorporation into consumer-friendly health information retrieval systems.MethodsFirst, we performed content analysis of the tags contributed, identifying a set of categories and refining the relational structure of the categories to develop a preliminary classification scheme, the Body Listening and Self-Management Taxonomy. Second, we compared the concepts in the Body Listening and Self-Management Taxonomy with concepts that were automatically identified from an extant health knowledge resource, the Unified Medical Language System (UMLS), to better characterize the information that participants contributed. Third, we employed visualization techniques to explore the concept space of the tags. A correlation matrix, based on the extent to which categories tended to be assigned to the same tags, was used to study the interrelatedness of the taxonomy categories. Then a network visualization was used to investigate structural relationships among the categories in the taxonomy.ResultsFirst, we proposed a taxonomy called the Body Listening and Self-Management Taxonomy, with four meta-level categories: (1) health management strategies, (2) concepts and states, (3) influencers, and (4) health-related information behavior. This taxonomy could inform future efforts to organize knowledge and content of this subject matter. Second, we compared the categories from this taxonomy with the UMLS concepts that were identified. Though the UMLS offers benefits such as speed and breadth of coverage, the Body Listening and Self-Management Taxonomy is more consumer-centric. Third, the correlation matrix and network visualization demonstrated that there are natural areas of ambiguity and semantic relatedness in the meanings of the concepts in the Body Listening and Self-Management Taxonomy. Use of these visualizations can be helpful in practice settings, to help library and information science practitioners understand and resolve potential challenges in classification; in research, to characterize the structure of the conceptual space of health management; and in the development of consumer-centric health information retrieval systems.ConclusionsA participatory platform can be employed to collect data concerning patient experiences of health management, which can in turn be used to develop new health knowledge resources or augment existing ones, as well as be incorporated into consume...

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Section: Discussionmentioning

confidence: 99%

The User Knows What to Call It: Incorporating Patient Voice Through User-Contributed Tags on a Participatory Platform About Health Management

Chen¹,

Carriere²,

Kaplan³

2017

J Med Internet Res

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“…Automated annotation can support users' tagging process, reduce their cognitive overhead, and help produce more stable, quality folksonomies on social media platforms [6]- [8]. It is natural to automatically annotate new documents with an existing collection of cleaned tags originally contributed by users.…”

Section: A Automated Social Text Annotationmentioning

confidence: 99%

“…Automatic social annotation is highly relevant to "tag recommendation" in the literature [8], which suggests tags from the list of candidates for different objects to support overall resource organization. Previous studies applied term frequency-based lexical features [9], adaptive hypergraph learning [6], and probabilistic graphical models [10], [11] to model the automated tagging process.…”

Section: Introductionmentioning

confidence: 99%

Automated Social Text Annotation With Joint Multilabel Attention Networks

Dong

Wang

Huang

et al. 2021

IEEE Trans. Neural Netw. Learning Syst.

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Automated social text annotation is the task of suggesting a set of tags for shared documents on social media platforms. The automated annotation process can reduce users' cognitive overhead in tagging and improve tag management for better search, browsing, and recommendation of documents. It can be formulated as a multilabel classification problem. We propose a novel deep learning-based method for this problem and design an attention-based neural network with semantic-based regularization, which can mimic users' reading and annotation behavior to formulate better document representation, leveraging the semantic relations among labels. The network separately models the title and the content of each document and injects an explicit, title-guided attention mechanism into each sentence. To exploit the correlation among labels, we propose two semantic-based loss regularizers, i.e., similarity and subsumption, which enforce the output of the network to conform to label semantics. The model with the semantic-based loss regularizers is referred to as the joint multilabel attention network (JMAN). We conducted a comprehensive evaluation study and compared JMAN to the state-of-the-art baseline models, using four large, real-world social media data sets. In terms of F 1 , JMAN significantly outperformed bidirectional gated recurrent unit (Bi-GRU) relatively by around 12.8%-78.6% and the hierarchical attention network (HAN) by around 3.9%-23.8%. The JMAN model demonstrates advantages in convergence and training speed. Further improvement of performance was observed against latent Dirichlet allocation (LDA) and support vector machine (SVM). When applying the semantic-based loss regularizers, the performance of HAN and Bi-GRU in terms of F 1 was also boosted. It is also found that dynamic update of the label semantic matrices (JMAN d) has

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“…Para evaluar la línea base y el modelo propuesto, se utilizaron las métricas estándar de precisión, recall (cobertura) y f-measure (medida f mejor conocida como F1) (Baeza-Yates & Ribeiro-Neto, 1999). La precisión mide la cantidad de etiquetas recomendadas que fueron usadas por el usuario para anotar el recurso, recall mide el número de etiquetas relevantes recomendadas sobre el total que debieron recomendarse y F1 es una medida que balancea precisión y recall.…”

Section: B Métricas De Evaluaciónunclassified

Un modelo híbrido de recomendación de etiquetas para sistemas de anotación social

Olvera¹,

Godoy²

2020

Enfoque UTE

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El etiquetado social consiste en clasificar recursos web, con el uso de palabras o etiquetas libremente elegidas por los usuarios. La simplicidad y apertura de los sistemas de etiquetado social para organizar recursos, es la clave de su éxito en Internet. Existen numerosos enfoques para facilitar al usuario el proceso de etiquetado, permitiéndole reutilizar etiquetas y optimizando así su limitado tiempo de lectura y escritura. Este documento propone un enfoque híbrido diferente, que resuelve de forma sencilla el problema de las recomendaciones basadas únicamente en el contenido del recurso, fusionando la lista de recomendaciones con las etiquetas más populares del historial de etiquetas del usuario, permitiéndole así reutilizar los términos asignados a otros recursos.

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A survey on tag recommendation methods

Cited by 58 publications

References 70 publications

The User Knows What to Call It: Incorporating Patient Voice Through User-Contributed Tags on a Participatory Platform About Health Management

The User Knows What to Call It: Incorporating Patient Voice Through User-Contributed Tags on a Participatory Platform About Health Management

Automated Social Text Annotation With Joint Multilabel Attention Networks

Un modelo híbrido de recomendación de etiquetas para sistemas de anotación social

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