Can all tags be used for search?

Bischoff, Kerstin; Firan, Claudiu S.; Nejdl, Wolfgang; Paiu, Raluca

doi:10.1145/1458082.1458112

Cited by 219 publications

(185 citation statements)

References 22 publications

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“…By translating and categorising these tags, we considerably reduced the set of tags that are not categorised from 40.8% to 32.4%. It is very important to highlight that the relative proportions of tags in each category are quite similar to those given in [7], where a non-noisy manual tag categorisation is carried out. This fact gives first insights about the validity of our proposal.…”

Section: Categorisation Resultsmentioning

confidence: 75%

Categorising Social Tags to Improve Folksonomy-Based Recommendations

2011

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Section: Categorisation Resultsmentioning

confidence: 75%

Categorising Social Tags to Improve Folksonomy-Based Recommendations

2011

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“…[1] analyzed the tag usage patterns, user activities and annotated resources in collaborative tagging systems. [2] did a comprehensive survey to systems like Del.icio.us, and Last.fm, to discover useful tags for information access. The second type mainly attempts to explore social annotations and link structures in folksonomy for various applications.…”

Section: Collaborative Tagging Systemmentioning

confidence: 99%

Tag-Group Based User Profiling for Personalized Search in Folksonomies

Liu

Huang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYWith the development of the Internet, there are more and more shared resources on the Web. Personalized search becomes increasingly important as users demand higher retrieval quality. Personalized search needs to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags (features) and thus provide a powerful way for organizing, retrieving and sharing different types of social resources. To capture and understand user preferences, a user is typically modeled as a vector of tag:value pairs (i.e., a tag-based user profile) in collaborative tagging systems. In such a tag-based user profile, a user's preference degree on a group of tags (i.e., a combination of several tags) mainly depends on the preference degree on every individual tag in the group. However, the preference degree on a combination of tags (a tag-group) cannot simply be obtained from linearly combining the preference on each tag. The combination of a user's two favorite tags may not be favorite for the user. In this article, we examine the limitations of previous tag-based personalized search. To overcome their problems, we model a user profile based on combinations of tags (tag-groups) and then apply it to the personalized search. By comparing it with the state-of-the-art methods, experimental results on a real data set shows the effectiveness of our proposed user profile method.

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“…Motivated by the previous works, Bischoff et al [7] manually classify a number of tag collections obtained from different social tagging systems (Flickr, Delicious, Last.fm) in several tag types, and study the distributions of tags assigned to each type, analysing their usage implications on search tasks. The obtained results provide insight into the use of different kinds of tags for improving search.…”

Section: Categorisation Of Social Tagsmentioning

confidence: 99%

“…Bischoff et al [7] compare several categorisation schemas. Table 1 summarises this comparison, and includes our categorisation, which fits with previous schemas.…”

Section: Folksonomy-based Recommender Systemsmentioning

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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Can all tags be used for search?

Cited by 219 publications

References 22 publications

Categorising Social Tags to Improve Folksonomy-Based Recommendations

Categorising Social Tags to Improve Folksonomy-Based Recommendations

Tag-Group Based User Profiling for Personalized Search in Folksonomies

Categorising social tags to improve folksonomy-based recommendations

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