Social tags for resource discovery: a comparison between machine learning and user-centric approaches

Razikin, Khasfariyati; Goh, Dion Hoe‐Lian; Chua, Alton Y. K.; Lee, Chei Sian

doi:10.1177/0165551511408847

Cited by 10 publications

(8 citation statements)

References 48 publications

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“…Some studies have compared and matched social tags with controlled vocabularies, applying various methods and measurements. Razikin, Goh, Chua, and Lee () examined tag effectiveness in document categorization using machine learning methods [i.e., term frequency–inverted document frequency (tf–idf)] and measured its effectiveness with precision, recall, and F‐measure. Lee and Schleyer () compared CiteULike tags and MeSH terms using similarity measurement (i.e., Jaccard similarity) and observed that tag terms and MeSH terms show different understandings of reader and indexer groups.…”

Section: Introductionmentioning

confidence: 99%

Finding subject terms for classificatory metadata from user‐generated social tags

Syn

Spring

2013

J Am Soc Inf Sci Tec

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With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata-generation process. Current research is exploring social tagging systems as a mechanism to allow nonprofessional catalogers to participate in metadata generation. Because social tags are not from controlled vocabularies, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research explores ways to obtain a set of tags representing the resource from the tags provided by users. Two metrics are introduced. Annotation Dominance (AD) is a measure of the extent to which a tag term is agreed to by users. Cross Resources Annotation Discrimination (CRAD) is a measure of a tag's potential to classify a collection. It is designed to remove tags that are used too broadly or narrowly. Using the proposed measurements, the research selects important tags (meta-terms) and removes meaningless ones (tag noise) from the tags provided by users. To evaluate the proposed approach to find classificatory metadata candidates, we rely on expert users' relevance judgments comparing suggested tag terms and expert metadata terms. The results suggest that processing of user tags using the two measurements successfully identifies the terms that represent the topic categories of web resource content. The suggested tag terms can be further examined in various usages as semantic metadata for the resources.

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Section: Introductionmentioning

confidence: 99%

Finding subject terms for classificatory metadata from user‐generated social tags

Syn

Spring

2013

J Am Soc Inf Sci Tec

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“…It should be noted that there are both useful and noisy tags in a tag set and to make use of tags it is important to identify the useful ones (Razikin et al, ; Tonkin, ). Tonkin () categorized tag forms into words, simple compounds, known encodings and unknown.…”

Section: Methodsmentioning

confidence: 99%

“…With this point of view, studies have compared user tags with controlled vocabularies, such as subject headings (Lee & Schleyer, 2010;Lu, Park, & Hu, 2010;Rolla, 2009;Yi & Chan, 2009), using different measurements for comparison including Jaccard similarity, TF-IDF, cosine-based similarity, etc. (Lee & Schleyer, 2010;Razikin et al, 2011;Yi, 2011).…”

Section: Approaches Of Using Social Tagsmentioning

confidence: 99%

“…The social tagging systems have introduced a new trend for users to organize and describe on the web resources. Much research focused on analyzing social tags as resource descriptions for various uses including indexing, information organization, information recommendation, and information discovery (Gupta, Li, Yin, & Han, ; Lin, Beaudoin, & Desai, ; Razikin, Goh, Chua, & Lee, ; Syn & Spring, ; Yi & Chan, ). However, different from expert generated controlled vocabularies, using social tags as a type of classification or resource description faces challenges due to its characteristics (Guy & Tonkin, ; Kipp & Campbell, ; Lin et al, ; Spiteri, ; Tonkin, ).…”

Section: Introductionmentioning

confidence: 99%

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Dealing with the long tail: Providing uniformity to compound tags

Syn

2014

Proc of Assoc for Info

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In using social tags, it has been a challenge to deal with the long tail of the tag distribution. A good portion of tag distribution includes compound tags. Based on the observation that the various forms of compound tags exist in a tag data set, this study tests a way to process various compound tag forms into a uniformed form. The tag uniformity process is introduced with the decomposition algorithm by identifying forms of compound tags. With the tag uniformity process, this study explores the potential of better use of social tags by reducing tag noise and shortening the long tail of the tag distribution. In addition, it is expected that the uniformity process will enhance the effectiveness of the measurements for tag use such as tag frequency. The results demonstrate that the tag uniformity process can successfully shorten the long tail and make the tag frequency precise as a measurement. Although the uniformity process may not solve all the issues related to the long tail, it can be implicated for improving tag analysis and services using tags. The benefits and limitations as well as potential implications of the tag uniformity process for tag analysis and use are discussed.

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“…Consequently, a user profile can also profit from these tags. In the last few years, a number of studies have tried to combine recommender systems with social tagging in a manner that can be highly beneficial in both areas [10][11][12]. Hybrid systems, employing this combination, are able to generate automated recommendations while retaining the flexibility of tags incorporating human-perceptive content [13].…”

Section: Introductionmentioning

confidence: 99%

A tag-based recommender system using rule-based collaborative profile enrichment

Movahedian

Khayyambashi

2014

IDA

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With the rapid increasing rate of the high volume of social web contents due to the growing popularity of social media services, significant attention has been drawn towards recommender systems i.e. systems, that offer recommendations to users on items appropriate to their requirements. To offer suitable recommendations, the systems need comprehensive user and item models that would be able to provide thorough understanding of their characteristics and preferences. In this article, a new recommender system is proposed based on the similarities between user and item profiles. The approach here is to generate user and item profiles by discovering frequent user-generated tag patterns, and to enrich each individual profile by a two-phase profile enrichment procedure. The profiles are extended by association rules discovered through the association rule mining process. The user/item profiles are further enriched through collaboration with other similar user/item profiles. To evaluate the performance of this proposed approach, a real dataset from The Del.icio.us website is used for empirical experiment. Experimental result s demonstrate that the proposed approach provides a better representation of user interests and achieves better recommendation results in terms of precision and ranking accuracy as compared to existing methods.

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Social tags for resource discovery: a comparison between machine learning and user-centric approaches

Cited by 10 publications

References 48 publications

Finding subject terms for classificatory metadata from user‐generated social tags

Finding subject terms for classificatory metadata from user‐generated social tags

Dealing with the long tail: Providing uniformity to compound tags

A tag-based recommender system using rule-based collaborative profile enrichment

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