A complete assessment of tagging quality: A consolidated methodology

Choi, Yunseon

doi:10.1002/asi.23198

Cited by 5 publications

(5 citation statements)

References 43 publications

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“…The idea is that a term used as tag many times tends to be a more "obvious" recommendation (if relevant at all), thus being of little use (if any) to improve the description of the target object provided by its tag set. We note that this concept is closely related to tag specificity, since rare words tend to be more specific (less general) [4]. Diversity, in turn, refers to the exhaustivity [4] of the set of recommended tags, which is defined as the coverage they provide for the topics of the target object.…”

Section: Motivationmentioning

confidence: 99%

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Relevance, Novelty, Diversity and Personalization in Tag Recommendation

Belém¹,

Almeida²,

Gonçalves³

2019

Anais Estendidos Do Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia)

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The design and evaluation of tag recommendation methods have historically focused on maximizing the relevance of the suggested tags for a given object, such as a movie or a song. Tag relevance can be defined in two perspectives. In an object-centered perspective, a tag is relevant if it correctly describes the content of the target object, while in a personalized perspective, a relevant tag not only describes well the content of the target object, but also matches the interests of the target user. However, even enriched by a personalized perspective, relevance by itself may not be enough to guarantee recommendation usefulness. Promoting novelty and diversity in tag recommendation not only increases the chances that the user will select some of the recommended tags, but also promotes complementary information (i.e., tags), which helps cover multiple aspects or topics related to the target object. Yet, no prior work has tackled novelty and diversity in the specific context of tag recommendation. In this thesis, we aim at proposing novel solutions that effectively address multiple aspects related to the tag recommendation problem, notably, relevance, novelty, diversity and personalization of the suggested tags. We evaluate our strategies using real data from five Web 2.0 applications, namely, Bibsonomy, LastFM, MovieLens, YahooVideo and YouTube. Our experimental results demonstrate the effectiveness of our new methods over state-of-the-art approaches, and attest the viability to effectively increase novelty and diversity with only a slight impact (if any) on relevance. We also found that our proposed syntactic attributes are responsible for significant improvements (up to 17% in precision) over the best relevance-driven method in a cold start scenario. In addition, we assessed the benefits of personalization to provide better descriptions of the target object, with average gains of 15% in relevance over the best object-centered approach.

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Section: Motivationmentioning

confidence: 99%

“…We note that this concept is closely related to tag specificity, since rare words tend to be more specific (less general) [4]. Diversity, in turn, refers to the exhaustivity [4] of the set of recommended tags, which is defined as the coverage they provide for the topics of the target object.…”

Section: Motivationmentioning

confidence: 99%

Relevance, Novelty, Diversity and Personalization in Tag Recommendation

Belém¹,

Almeida²,

Gonçalves³

2019

Anais Estendidos Do Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia)

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“…We also note that this definition of novelty is closely related to tag specificity , given that rare words tend to be more specific (less general). According to Baeza‐Yates and Ribeiro‐Neto () as well as Choi (), specificity is a property of the term semantics, that is, a term or tag is more or less specific depending on its meaning. For example, “feline” is less specific than “cat” or “persian.” One would expect that the most general term feline would be used to describe a larger number of objects than the more specific terms.…”

Section: Contextualization and Problem Statementmentioning

confidence: 99%

“…However, we chose the term “novelty” instead of “specificity” to keep consistency with the general recommendation literature (Celma & Herrera, ; Vargas & Castells, ; Zhang, Séaghdha, Quercia, & Jambor, ), which estimates novelty similarly. A related property of object descriptions is the exhaustivity , which is defined as the coverage they provide for the main topics of the object (Baeza‐Yates & Ribeiro‐Neto, ; Choi, ). This fits exactly in our diversity concept, as we will discuss below.…”

Section: Contextualization and Problem Statementmentioning

confidence: 99%

A survey on tag recommendation methods

Belém

Almeida

Gonçalves

2016

Asso for Info Science & Tech

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Tags (keywords freely assigned by users to describe web content) have become highly popular on Web 2.0 applications, because of the strong stimuli and easiness for users to create and describe their own content. This increase in tag popularity has led to a vast literature on tag recommendation methods. These methods aim at assisting users in the tagging process, possibly increasing the quality of the generated tags and, consequently, improving the quality of the information retrieval (IR) services that rely on tags as data sources. Regardless of the numerous and diversified previous studies on tag recommendation, to our knowledge, no previous work has summarized and organized them into a single survey article. In this article, we propose a taxonomy for tag recommendation methods, classifying them according to the target of the recommendations, their objectives, exploited data sources, and underlying techniques. Moreover, we provide a critical overview of these methods, pointing out their advantages and disadvantages. Finally, we describe the main open challenges related to the field, such as tag ambiguity, cold start, and evaluation issues.

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“…Efforts to leverage “collective intelligence” include the collective authoring of Wikipedia content, shared tagging of photos on Flickr, sharing of bookmarks on Del.icio.us, and collective annotation of museum artifacts [ 1 - 5 ]. Collaborative tagging, a practice in which users add meta-data to shared content, can be useful when personnel are not readily available to perform classification tasks [ 6 ], as a channel for nonprofessional catalogers to participate in meta-data creation [ 7 ], and as an approach to organize knowledge by users’ own language [ 8 ].…”

Section: Introductionmentioning

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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A complete assessment of tagging quality: A consolidated methodology

Cited by 5 publications

References 43 publications

Relevance, Novelty, Diversity and Personalization in Tag Recommendation

Relevance, Novelty, Diversity and Personalization in Tag Recommendation

A survey on tag recommendation methods

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

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