Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering

Gemmell, Jonathan; Shepitsen, Andriy; Mobasher, Bamshad; Burke, Robin

doi:10.1007/978-3-540-85836-2_19

Cited by 68 publications

(47 citation statements)

References 12 publications

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“…Similarly, Gemmell, Shepitsen, Mobasher and Burke explore in several works [14][31] strategies that cluster the entire space of tags to obtain sets of (semantically) related tags. These clusters may represent coherent topic areas.…”

Section: Folksonomy-based Recommender Systemsmentioning

confidence: 99%

“…In this way, we tend to favour the latter category of friendships under the assumption that they are more concrete, similar to real life. What is more, bidirectional friendships are usually the norm in many other social networks such as Facebook 16 and 13 Google translate, http://translate.google.com 14 Note that due to ambiguity, a tag may belong to more than one category. See Section 6.5 for more details.…”

Section: Datasetmentioning

confidence: 99%

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Categorising Social Tags to Improve Folksonomy-Based Recommendations

2011

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Section: Folksonomy-based Recommender Systemsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Categorising Social Tags to Improve Folksonomy-Based Recommendations

2011

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“…Such views are more succinct and informative than the original networks. It is for this reason that community detection has found applications in the field of recommendation systems [15][16][17][18], as well as for representing user profiles [19,20]. Other applications that make use of the knowledge extracted from tag communities include sense disambiguation [21] and ontology evolution/population [16].…”

Section: Community Trend and Event Detectionmentioning

confidence: 99%

Extracting Emergent Semantics from Large-Scale User-Generated Content

Kompatsiaris

Diplaris

Papadopoulos

2012

Advances in Intelligent and Soft Computing

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Abstract. This paper presents a survey of novel technologies for uncovering implicit knowledge through the analysis of user-contributed content in Web2.0 applications. The special features of emergent semantics are herein described, along with the various dimensions that the techniques should be able to handle. Consequently a series of application domains is given where the extracted information can be consumed. The relevant techniques are reviewed and categorised according to their capability for scaling, multi-modal analysis, social networks analysis, semantic representation, real-time and spatio-temporal processing. A showcase of such an emergent semantics extraction application, namely ClustTour, is also presented, and open issues and future challenges in this new field are discussed.

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“…The outcome of this filtering process was a reduced data collection of 2022 users, 24263 items and 41742 tags (20055 content-based, 8300 context-based, 9013 subjective, and 4374 organisational 14 ), with the derived sub-matrices" densities shown in Table 4, and which is as well made available at (blind for review).…”

Section: Data Collectionmentioning

confidence: 99%

Categorising social tags to improve folksonomy-based recommendations

Cantador

Konstas

Jose

2011

Journal of Web Semantics

102

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This is the author’s version of a work that was accepted for publication in Web Semantics: Science, Services and Agents on the World Wide Web. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Web Semantics: Science, Services and Agents on the World Wide Web, 9, 1, (2011) DOI: 10.1016/j.websem.2010.10.001In social tagging systems, users have different purposes when they annotate items. Tags not only depict the content of the annotated items, for example by listing the objects that appear in a photo, or express contextual information about the items, for example by providing the location or the time in which a photo was taken, but also describe subjective qualities and opinions about the items, or can be related to organisational aspects, such as self-references and personal tasks. Current folksonomy-based search and recommendation models exploit the social tag space as a whole to retrieve those items relevant to a tag-based query or user profile, and do not take into consideration the purposes of tags. We hypothesise that a significant percentage of tags are noisy for content retrieval, and believe that the distinction of the personal intentions underlying the tags may be beneficial to improve the accuracy of search and recommendation processes. We present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Our approach finds the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. The obtained concepts are then transformed into semantic classes that can be uniquely assigned to content- and context-based categories. The identification of subjective and organisational tags is based on natural language processing heuristics. We collected a representative dataset from Flickr social tagging system, and conducted an empirical study to categorise real tagging data, and evaluate whether the resultant tags categories really benefit a recommendation model using the Random Walk with Restarts method. The results show that content- and context-based tags are considered superior to subjective and organisational tags, achieving equivalent performance to using the whole tag space.This research was supported by the European Commission (SALERO, FP6-027122), by the Spanish Ministry of Science and Education (RIM3, TIN2008-06566-C04-02), and by the Spanish Ministry of Industry (i3media, CENIT-2007-1012)

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Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering

Cited by 68 publications

References 12 publications

Categorising Social Tags to Improve Folksonomy-Based Recommendations

Categorising Social Tags to Improve Folksonomy-Based Recommendations

Extracting Emergent Semantics from Large-Scale User-Generated Content

Categorising social tags to improve folksonomy-based recommendations

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