Categorising Social Tags to Improve Folksonomy-Based Recommendations

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Categorising Social Tags to Improve Folksonomy-Based Recommendations
Authors
Iván Cantador
Ioannis Konstas
Joemon M. Jose
Publication date
2011
ISSN
15708268
DOI
10.1016/j.websem.2010.10.001
Links

Categorising Social Tags to Improve Folksonomy-Based Recommendations - scientific work about Wikipedia quality published in 2011, written by Iván Cantador, Ioannis Konstas and Joemon M. Jose.

Overview

In 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. Authors 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. Authors present a mechanism to automatically filter and classify raw tags in a set of purpose-oriented categories. Their 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. Authors 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. © 2010 Elsevier B.V. All rights reserved.

Embed

Wikipedia Quality

Cantador, Iván; Konstas, Ioannis; Jose, Joemon M.. (2011). "[[Categorising Social Tags to Improve Folksonomy-Based Recommendations]]". Journal of Web Semantics Volume 9, Issue 1, March 2011, pp. 1-15. ISSN: 15708268. DOI: 10.1016/j.websem.2010.10.001.

English Wikipedia

{{cite journal |last1=Cantador |first1=Iván |last2=Konstas |first2=Ioannis |last3=Jose |first3=Joemon M. |title=Categorising Social Tags to Improve Folksonomy-Based Recommendations |date=2011 |issn=15708268 |doi=10.1016/j.websem.2010.10.001 |url=https://wikipediaquality.com/wiki/Categorising_Social_Tags_to_Improve_Folksonomy-Based_Recommendations |journal=Journal of Web Semantics Volume 9, Issue 1, March 2011, pp. 1-15}}

HTML

Cantador, Iván; Konstas, Ioannis; Jose, Joemon M.. (2011). &quot;<a href="https://wikipediaquality.com/wiki/Categorising_Social_Tags_to_Improve_Folksonomy-Based_Recommendations">Categorising Social Tags to Improve Folksonomy-Based Recommendations</a>&quot;. Journal of Web Semantics Volume 9, Issue 1, March 2011, pp. 1-15. ISSN: 15708268. DOI: 10.1016/j.websem.2010.10.001.